Merge branch 'master' into issue-11374

Signed-off-by: JAYICE <1185430411@qq.com>
app/vmui: fix custom step synchronization between state and URL (#11350 )
2026-08-12 18:52:30 +03:00 · 2026-08-11 13:44:02 +08:00 · 2026-08-10 19:29:10 +03:00 · 2026-08-10 19:22:11 +03:00 · 2026-08-10 14:38:51 +03:00 · 2026-08-10 14:28:01 +03:00
56 changed files with 1264 additions and 707 deletions
--- a/app/vmalert/rule/group.go
+++ b/app/vmalert/rule/group.go
@@ -290,6 +290,8 @@ func (g *Group) updateWith(newGroup *Group) error {
 	g.Headers = newGroup.Headers
 	g.NotifierHeaders = newGroup.NotifierHeaders
 	g.Labels = newGroup.Labels
+	g.EvalDelay = newGroup.EvalDelay
+	g.evalAlignment = newGroup.evalAlignment
 	g.Limit = newGroup.Limit
 	g.checksum = newGroup.checksum
 	g.Rules = newRules
--- a/app/vmalert/rule/group_test.go
+++ b/app/vmalert/rule/group_test.go
@@ -78,6 +78,12 @@ func TestUpdateWith(t *testing.T) {
 		if g.Debug != expect.Debug {
 			t.Fatalf("expected to have debug %v; got %v", expect.Debug, g.Debug)
 		}
+		if !durationPtrEqual(g.EvalDelay, expect.EvalDelay) {
+			t.Fatalf("expected to have eval_delay %v; got %v", expect.EvalDelay, g.EvalDelay)
+		}
+		if !boolPtrEqual(g.evalAlignment, expect.evalAlignment) {
+			t.Fatalf("expected to have eval_alignment %v; got %v", expect.evalAlignment, g.evalAlignment)
+		}
 	}

 	// new rule
@@ -237,6 +243,37 @@ func TestUpdateWith(t *testing.T) {
 			{Alert: "foo1", Debug: &debug},
 		},
 	})
+
+	// update group evaluation settings
+	evalDelay := promutil.NewDuration(time.Minute)
+	evalAlignment := false
+	f(config.Group{
+		Rules: []config.Rule{{
+			Record: "foo",
+			Expr:   "max(up)",
+		}},
+	}, config.Group{
+		EvalDelay:     evalDelay,
+		EvalAlignment: &evalAlignment,
+		Rules: []config.Rule{{
+			Record: "foo",
+			Expr:   "min(up)",
+		}},
+	})
+}
+
+func durationPtrEqual(a, b *time.Duration) bool {
+	if a == nil || b == nil {
+		return a == b
+	}
+	return *a == *b
+}
+
+func boolPtrEqual(a, b *bool) bool {
+	if a == nil || b == nil {
+		return a == b
+	}
+	return *a == *b
 }

 func TestUpdateDuringRandSleep(t *testing.T) {
--- a/app/vmui/packages/vmui/assets/favicon.svg
+++ b/app/vmui/packages/vmui/assets/favicon.svg
@@ -0,0 +1,5 @@
+<svg width="48" height="48" fill="#020202" xmlns="http://www.w3.org/2000/svg">
+    <path d="M24.5475 0C10.3246.0265251 1.11379 3.06365 4.40623 6.10077c0 0 12.32997 11.23333 16.58217 14.84083.8131.6896 2.1728 1.1936 3.5191 1.2201h.1199c1.3463-.0265 2.706-.5305 3.5191-1.2201 4.2522-3.5942 16.5422-14.84083 16.5422-14.84083C48.0478 3.06365 38.8636.0265251 24.6674 0"/>
+    <path d="M28.1579 27.0159c-.8131.6896-2.1728 1.1936-3.5191 1.2201h-.12c-1.3463-.0265-2.7059-.5305-3.519-1.2201-2.9725-2.5067-13.35639-11.87-17.26201-15.3979v5.4112c0 .5968.22661 1.3793.6265 1.7506C7.00358 21.1936 17.2675 30.5437 20.9731 33.6737c.8132.6896 2.1728 1.1936 3.5191 1.2201h.12c1.3463-.0265 2.7059-.5305 3.519-1.2201 3.679-3.13 13.9429-12.4536 16.6089-14.8939.4132-.3713.6265-1.1538.6265-1.7506V11.618c-3.9323 3.5411-14.3162 12.931-17.2354 15.3979h.0267Z"/>
+    <path d="M28.1579 39.748c-.8131.6897-2.1728 1.1937-3.5191 1.2202h-.12c-1.3463-.0265-2.7059-.5305-3.519-1.2202-2.9725-2.4933-13.35639-11.8567-17.26201-15.3978v5.4111c0 .5969.22661 1.3793.6265 1.7507C7.00358 33.9258 17.2675 43.2759 20.9731 46.4058c.8132.6897 2.1728 1.1937 3.5191 1.2202h.12c1.3463-.0265 2.7059-.5305 3.519-1.2202 3.679-3.1299 13.9429-12.4535 16.6089-14.8938.4132-.3714.6265-1.1538.6265-1.7507v-5.4111c-3.9323 3.5411-14.3162 12.931-17.2354 15.3978h.0267Z"/>
+</svg>
--- a/app/vmui/packages/vmui/index.html
+++ b/app/vmui/packages/vmui/index.html
@@ -2,9 +2,9 @@
 <html lang="en">
 <head>
  <meta charset="utf-8"/>
-  <link rel="icon" href="/favicon.svg"/>
-  <link rel="apple-touch-icon" href="/favicon.svg"/>
-  <link rel="mask-icon" href="/favicon.svg" color="#000000">
+  <link id="favicon" rel="icon" href="/assets/favicon.svg" />
+  <link rel="apple-touch-icon" href="/assets/favicon.svg" />
+  <link id="mask-icon" rel="mask-icon" href="/assets/favicon.svg?no-inline" color="#000000">

  <meta name="robots" content="noindex">
  <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=5"/>
--- a/app/vmui/packages/vmui/public/favicon.svg
+++ b/app/vmui/packages/vmui/public/favicon.svg
@@ -1 +0,0 @@
-<svg width="48" height="48" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M24.5475 0C10.3246.0265251 1.11379 3.06365 4.40623 6.10077c0 0 12.32997 11.23333 16.58217 14.84083.8131.6896 2.1728 1.1936 3.5191 1.2201h.1199c1.3463-.0265 2.706-.5305 3.5191-1.2201 4.2522-3.5942 16.5422-14.84083 16.5422-14.84083C48.0478 3.06365 38.8636.0265251 24.6674 0" fill="#020202"/><path d="M28.1579 27.0159c-.8131.6896-2.1728 1.1936-3.5191 1.2201h-.12c-1.3463-.0265-2.7059-.5305-3.519-1.2201-2.9725-2.5067-13.35639-11.87-17.26201-15.3979v5.4112c0 .5968.22661 1.3793.6265 1.7506C7.00358 21.1936 17.2675 30.5437 20.9731 33.6737c.8132.6896 2.1728 1.1936 3.5191 1.2201h.12c1.3463-.0265 2.7059-.5305 3.519-1.2201 3.679-3.13 13.9429-12.4536 16.6089-14.8939.4132-.3713.6265-1.1538.6265-1.7506V11.618c-3.9323 3.5411-14.3162 12.931-17.2354 15.3979h.0267Z" fill="#020202"/><path d="M28.1579 39.748c-.8131.6897-2.1728 1.1937-3.5191 1.2202h-.12c-1.3463-.0265-2.7059-.5305-3.519-1.2202-2.9725-2.4933-13.35639-11.8567-17.26201-15.3978v5.4111c0 .5969.22661 1.3793.6265 1.7507C7.00358 33.9258 17.2675 43.2759 20.9731 46.4058c.8132.6897 2.1728 1.1937 3.5191 1.2202h.12c1.3463-.0265 2.7059-.5305 3.519-1.2202 3.679-3.1299 13.9429-12.4535 16.6089-14.8938.4132-.3714.6265-1.1538.6265-1.7507v-5.4111c-3.9323 3.5411-14.3162 12.931-17.2354 15.3978h.0267Z" fill="#020202"/></svg>
--- a/app/vmui/packages/vmui/public/manifest.json
+++ b/app/vmui/packages/vmui/public/manifest.json
@@ -3,7 +3,7 @@
  "name": "vmui",
  "icons": [
    {
-      "src": "favicon.svg",
+      "src": "./assets/favicon.svg",
      "sizes": "any",
      "type": "image/svg+xml"
    }
--- a/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/BrowserTabController/BrowserTabController.tsx
+++ b/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/BrowserTabController/BrowserTabController.tsx
@@ -0,0 +1,61 @@
+import { FC, useMemo } from "preact/compat";
+import "./style.scss";
+import { createFaviconUrl } from "../../../../utils/favicon";
+import classNames from "classnames";
+import { useBrowserTabSync } from "./hooks/useBrowserTabSync";
+import { CloseIcon } from "../../../Main/Icons";
+import { faviconColors } from "../../../../constants/faviconColors";
+
+const BrowserTabController: FC = () => {
+  const { faviconColor, changeFaviconColor } = useBrowserTabSync();
+
+  const faviconUrl = useMemo(() => {
+    return createFaviconUrl(faviconColor);
+  }, [faviconColor]);
+
+  const createHandlerClick = (color?: string) => () => {
+    changeFaviconColor(color);
+  };
+
+  return (
+    <div className="vm-browser-tab-controller">
+      <p className="vm-server-configurator__title">Favicon color</p>
+
+      <div className="vm-browser-tab-controller-palette">
+        <div className="vm-browser-tab-controller-palette-list">
+          <button
+            className="vm-browser-tab-controller-palette-list__item vm-browser-tab-controller-palette-list__item_reset"
+            type="button"
+            onClick={createHandlerClick()}
+            aria-label="Reset favicon color"
+          >
+            <CloseIcon />
+          </button>
+
+          {faviconColors.map(color => (
+            <button
+              className={classNames({
+                "vm-browser-tab-controller-palette-list__item": true,
+                "vm-browser-tab-controller-palette-list__item_selected": faviconColor === color
+              })}
+              key={color}
+              type="button"
+              style={{ color }}
+              onClick={createHandlerClick(color)}
+              aria-label={`Set favicon color to ${color}`}
+              aria-pressed={faviconColor === color}
+            />
+          ))}
+        </div>
+
+        <img
+          className="vm-browser-tab-controller-palette__preview"
+          src={faviconUrl}
+          alt="Favicon preview"
+        />
+      </div>
+    </div>
+  );
+};
+
+export default BrowserTabController;
--- a/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/BrowserTabController/hooks/useBrowserTabSync.ts
+++ b/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/BrowserTabController/hooks/useBrowserTabSync.ts
@@ -0,0 +1,41 @@
+import useEventListener from "../../../../../hooks/useEventListener";
+import { useEffect, useState } from "preact/compat";
+import { getFromStorage, removeFromStorage, saveToStorage } from "../../../../../utils/storage";
+import { getFaviconStorageKey, updateFaviconColor } from "../../../../../utils/favicon";
+
+const storageKey = `FAVICON_COLOR:${getFaviconStorageKey()}` as const;
+
+const getColorFromStorage = () => {
+  return getFromStorage(storageKey) as string | undefined;
+};
+
+export const useBrowserTabSync = () => {
+  const [faviconColor, setFaviconColor] = useState(getColorFromStorage);
+
+  const handleUpdateColor = () => {
+    setFaviconColor(getColorFromStorage());
+  };
+
+  const changeFaviconColor = (color?: string) => {
+    if (color) {
+      saveToStorage(storageKey, color);
+    } else {
+      removeFromStorage([storageKey]);
+    }
+  };
+
+  useEffect(() => {
+    handleUpdateColor();
+  }, []);
+
+  useEffect(() => {
+    updateFaviconColor(faviconColor);
+  }, [faviconColor]);
+
+  useEventListener("storage", handleUpdateColor);
+
+  return {
+    faviconColor,
+    changeFaviconColor,
+  };
+};
--- a/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/BrowserTabController/style.scss
+++ b/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/BrowserTabController/style.scss
@@ -0,0 +1,71 @@
+@use "src/styles/variables" as *;
+
+$color-item-size: 28px;
+$outline-width: 2px;
+$outline-offset: 2px;
+$outline-space: $outline-width + $outline-offset;
+
+.vm-browser-tab-controller {
+  .vm-server-configurator__title {
+    padding-bottom: 2px;
+  }
+
+  &-palette {
+    display: flex;
+    align-items: flex-start;
+    justify-content: space-between;
+    gap: calc($padding-large * 2);
+
+    &-list {
+      display: flex;
+      align-items: center;
+      justify-content: flex-start;
+      flex-wrap: wrap;
+      gap: $padding-small;
+
+      &__item {
+        width: $color-item-size;
+        height: $color-item-size;
+        aspect-ratio: 1;
+        border-radius: 50%;
+        background-color: currentColor;
+        cursor: pointer;
+        transition-property: transform;
+        transition-duration: 0.15s;
+        transition-timing-function: linear;
+
+        &:hover {
+          transform: scale(1.1);
+        }
+
+        &:focus-visible {
+          transform: scale(1.1);
+        }
+
+        &_reset {
+          display: flex;
+          align-items: center;
+          justify-content: center;
+          background: transparent;
+          border: $border-divider;
+          color: $color-text-disabled;
+          padding: calc($padding-small / 2);
+        }
+
+        &_selected {
+          width: $color-item-size - 2 * $outline-space;
+          height: $color-item-size - 2 * $outline-space;
+          margin: $outline-space;
+          outline: $outline-width solid currentColor;
+          outline-offset: $outline-offset;
+          pointer-events: none;
+        }
+      }
+    }
+
+    &__preview {
+      width: $color-item-size;
+      height: auto;
+    }
+  }
+}
--- a/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/GlobalSettings.tsx
+++ b/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/GlobalSettings.tsx
@@ -8,10 +8,12 @@ import Tooltip from "../../Main/Tooltip/Tooltip";
 import LimitsConfigurator from "./LimitsConfigurator/LimitsConfigurator";
 import { getAppModeEnable } from "../../../utils/app-mode";
 import classNames from "classnames";
-import Timezones from "./Timezones/Timezones";
+import TimezonesPicker from "./Timezones/TimezonesPicker";
 import ThemeControl from "../ThemeControl/ThemeControl";
 import useDeviceDetect from "../../../hooks/useDeviceDetect";
 import useBoolean from "../../../hooks/useBoolean";
+import BrowserTabController from "./BrowserTabController/BrowserTabController";
+import LegendCollapseController from "./LegendCollapseController/LegendCollapseController";

 const title = "Settings";

@@ -26,7 +28,6 @@ const GlobalSettings: FC = () => {

  const serverSettingRef = useRef<ChildComponentHandle>(null);
  const limitsSettingRef = useRef<ChildComponentHandle>(null);
-  const timezoneSettingRef = useRef<ChildComponentHandle>(null);

  const {
    value: open,
@@ -37,7 +38,6 @@ const GlobalSettings: FC = () => {
  const handleApply = () => {
    serverSettingRef.current && serverSettingRef.current.handleApply();
    limitsSettingRef.current && limitsSettingRef.current.handleApply();
-    timezoneSettingRef.current && timezoneSettingRef.current.handleApply();
    handleClose();
  };

@@ -49,6 +49,10 @@ const GlobalSettings: FC = () => {
        onClose={handleClose}
      />
    },
+    {
+      show: true,
+      component: <TimezonesPicker/>
+    },
    {
      show: true,
      component: <LimitsConfigurator
@@ -58,12 +62,16 @@ const GlobalSettings: FC = () => {
    },
    {
      show: true,
-      component: <Timezones ref={timezoneSettingRef}/>
+      component: <LegendCollapseController/>
    },
    {
      show: !appModeEnable,
      component: <ThemeControl/>
-    }
+    },
+    {
+      show: true,
+      component: <BrowserTabController/>
+    },
  ].filter(control => control.show);

  return <>
--- a/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/LegendCollapseController/LegendCollapseController.tsx
+++ b/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/LegendCollapseController/LegendCollapseController.tsx
@@ -0,0 +1,30 @@
+import { FC, useEffect, useState } from "preact/compat";
+import { getFromStorage, saveToStorage } from "../../../../utils/storage";
+import Switch from "../../../Main/Switch/Switch";
+import { LEGEND_COLLAPSE_SERIES_LIMIT } from "../../../../constants/graph";
+import "./style.scss";
+
+const LegendCollapseController: FC = () => {
+  const storageCollapse = getFromStorage("LEGEND_AUTO_COLLAPSE");
+  const [legendCollapse, setLegendCollapse] = useState(storageCollapse ? storageCollapse === "true" : true);
+
+  useEffect(() => {
+    saveToStorage("LEGEND_AUTO_COLLAPSE", `${legendCollapse}`);
+  }, [legendCollapse]);
+
+  return (
+    <div className="vm-legend-collapse-controller">
+      <Switch
+        fullWidth
+        color="neutral"
+        value={legendCollapse}
+        onChange={setLegendCollapse}
+        label={<span className="vm-server-configurator__title">Auto-collapse legend</span>}
+      />
+      <span className="vm-legend-collapse-controller__description">
+        Collapses the legend when series count exceeds {LEGEND_COLLAPSE_SERIES_LIMIT} to reduce UI load.
+      </span>
+    </div>);
+};
+
+export default LegendCollapseController;
--- a/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/LegendCollapseController/style.scss
+++ b/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/LegendCollapseController/style.scss
@@ -0,0 +1,19 @@
+@use "src/styles/variables" as *;
+
+.vm-legend-collapse-controller {
+  background-color: $color-hover-black;
+  border-radius: $border-radius-medium;
+  padding: $padding-large;
+  border: $border-divider;
+
+  .vm-graph-settings-row__label {
+    margin: 0;
+  }
+
+  &__description {
+    padding-top: $padding-global;
+    font-size: $font-size-small;
+    line-height: 1.3;
+    text-wrap: pretty;
+  }
+}
--- a/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/LimitsConfigurator/LimitsConfigurator.tsx
+++ b/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/LimitsConfigurator/LimitsConfigurator.tsx
@@ -1,23 +1,21 @@
-import { forwardRef, useCallback, useEffect, useImperativeHandle, useState } from "preact/compat";
+import { forwardRef, useCallback, useImperativeHandle, useState } from "preact/compat";
 import { DisplayType, ErrorTypes } from "../../../../types";
 import TextField from "../../../Main/TextField/TextField";
 import Tooltip from "../../../Main/Tooltip/Tooltip";
 import { InfoIcon, RestartIcon } from "../../../Main/Icons";
 import Button from "../../../Main/Button/Button";
-import { DEFAULT_MAX_SERIES, LEGEND_COLLAPSE_SERIES_LIMIT } from "../../../../constants/graph";
+import { DEFAULT_MAX_SERIES } from "../../../../constants/graph";
 import "./style.scss";
 import classNames from "classnames";
 import useDeviceDetect from "../../../../hooks/useDeviceDetect";
 import { ChildComponentHandle } from "../GlobalSettings";
 import { useCustomPanelDispatch, useCustomPanelState } from "../../../../state/customPanel/CustomPanelStateContext";
-import Switch from "../../../Main/Switch/Switch";
-import { getFromStorage, saveToStorage } from "../../../../utils/storage";

 interface ServerConfiguratorProps {
-  onClose: () => void
+  onClose: () => void;
 }

-const fields: {label: string, type: DisplayType}[] = [
+const fields: { label: string, type: DisplayType }[] = [
  { label: "Graph", type: DisplayType.chart },
  { label: "JSON", type: DisplayType.code },
  { label: "Table", type: DisplayType.table }
@@ -29,8 +27,7 @@ const LimitsConfigurator = forwardRef<ChildComponentHandle, ServerConfiguratorPr
  const { seriesLimits } = useCustomPanelState();
  const customPanelDispatch = useCustomPanelDispatch();

-  const storageCollapse = getFromStorage("LEGEND_AUTO_COLLAPSE");
-  const [legendCollapse, setLegendCollapse] = useState(storageCollapse ? storageCollapse === "true" : true);
+

  const [limits, setLimits] = useState(seriesLimits);
  const [error, setError] = useState({
@@ -43,7 +40,7 @@ const LimitsConfigurator = forwardRef<ChildComponentHandle, ServerConfiguratorPr
    setLimits(DEFAULT_MAX_SERIES);
  };

-  const createChangeHandler = (type: DisplayType) =>  (val: string) => {
+  const createChangeHandler = (type: DisplayType) => (val: string) => {
    const value = val || "";
    setError(prev => ({ ...prev, [type]: +value < 0 ? ErrorTypes.positiveNumber : "" }));
    setLimits({
@@ -57,10 +54,6 @@ const LimitsConfigurator = forwardRef<ChildComponentHandle, ServerConfiguratorPr
    onClose();
  }, [limits]);

-  useEffect(() => {
-    saveToStorage("LEGEND_AUTO_COLLAPSE", `${legendCollapse}`);
-  }, [legendCollapse]);
-
  useImperativeHandle(ref, () => ({ handleApply }), [handleApply]);

  return (
@@ -106,19 +99,6 @@ const LimitsConfigurator = forwardRef<ChildComponentHandle, ServerConfiguratorPr
          </div>
        ))}
      </div>
-
-      <div className="vm-graph-settings-row">
-        <span className="vm-graph-settings-row__label">Auto-collapse legend</span>
-        <Switch
-          value={legendCollapse}
-          onChange={setLegendCollapse}
-          label={legendCollapse ? "Enabled" : "Disabled"}
-          fullWidth={isMobile}
-        />
-        <span className="vm-legend-configs-item__info">
-          Collapses the legend when series count exceeds {LEGEND_COLLAPSE_SERIES_LIMIT} to reduce UI load.
-        </span>
-      </div>
    </div>
  );
 });
--- a/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/Timezones/Timezones.tsx
+++ b/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/Timezones/Timezones.tsx
@@ -1,183 +0,0 @@
-import { FC, forwardRef, useEffect, useImperativeHandle, useMemo, useRef, useState } from "preact/compat";
-import { getBrowserTimezone, getTimezoneList, getUTCByTimezone } from "../../../../utils/time";
-import { ArrowDropDownIcon } from "../../../Main/Icons";
-import classNames from "classnames";
-import Popper from "../../../Main/Popper/Popper";
-import Accordion from "../../../Main/Accordion/Accordion";
-import TextField from "../../../Main/TextField/TextField";
-import { Timezone } from "../../../../types";
-import "./style.scss";
-import useDeviceDetect from "../../../../hooks/useDeviceDetect";
-import useBoolean from "../../../../hooks/useBoolean";
-import WarningTimezone from "./WarningTimezone";
-import { useTimeDispatch, useTimeState } from "../../../../state/time/TimeStateContext";
-
-interface PinnedTimezone extends Timezone {
-  title: string;
-  isInvalid?: boolean;
-}
-
-const browserTimezone = getBrowserTimezone();
-
-const Timezones: FC = forwardRef((props, ref) => {
-  const { isMobile } = useDeviceDetect();
-  const timezones = getTimezoneList();
-
-  const { timezone: stateTimezone, defaultTimezone } = useTimeState();
-  const timeDispatch = useTimeDispatch();
-
-  const [timezone, setTimezone] = useState(stateTimezone);
-  const [search, setSearch] = useState("");
-  const targetRef = useRef<HTMLDivElement>(null);
-
-  const {
-    value: openList,
-    toggle: toggleOpenList,
-    setFalse: handleCloseList,
-  } = useBoolean(false);
-
-  const pinnedTimezones = useMemo(() => [
-    {
-      title: `Default time (${defaultTimezone})`,
-      region: defaultTimezone,
-      utc: defaultTimezone ? getUTCByTimezone(defaultTimezone) : "UTC"
-    },
-    {
-      title: browserTimezone.title,
-      region: browserTimezone.region,
-      utc: getUTCByTimezone(browserTimezone.region),
-      isInvalid: !browserTimezone.isValid
-    },
-    {
-      title: "UTC (Coordinated Universal Time)",
-      region: "UTC",
-      utc: "UTC"
-    },
-  ].filter(t => t.region) as PinnedTimezone[], [defaultTimezone]);
-
-  const searchTimezones = useMemo(() => {
-    if (!search) return timezones;
-    try {
-      return getTimezoneList(search);
-    } catch (e) {
-      return {};
-    }
-  }, [search, timezones]);
-
-  const timezonesGroups = useMemo(() => Object.keys(searchTimezones), [searchTimezones]);
-
-  const activeTimezone = useMemo(() => ({
-    region: timezone,
-    utc: getUTCByTimezone(timezone)
-  }), [timezone]);
-
-  const handleChangeSearch = (val: string) => {
-    setSearch(val);
-  };
-
-  const handleSetTimezone = (val: Timezone) => {
-    setTimezone(val.region);
-    setSearch("");
-    handleCloseList();
-  };
-
-  const createHandlerSetTimezone = (val: Timezone) => () => {
-    handleSetTimezone(val);
-  };
-
-  useEffect(() => {
-    setTimezone(stateTimezone);
-  }, [stateTimezone]);
-
-  useImperativeHandle(ref, () => ({
-    handleApply: () => {
-      timeDispatch({ type: "SET_TIMEZONE", payload: timezone });
-    }
-  }), [timezone]);
-
-  return (
-    <div className="vm-timezones">
-      <div className="vm-server-configurator__title">
-        Time zone
-      </div>
-      <div
-        className="vm-timezones-item vm-timezones-item_selected"
-        onClick={toggleOpenList}
-        ref={targetRef}
-      >
-        <div className="vm-timezones-item__title">{activeTimezone.region}</div>
-        <div className="vm-timezones-item__utc">{activeTimezone.utc}</div>
-        <div
-          className={classNames({
-            "vm-timezones-item__icon": true,
-            "vm-timezones-item__icon_open": openList
-          })}
-        >
-          <ArrowDropDownIcon/>
-        </div>
-      </div>
-      <Popper
-        open={openList}
-        buttonRef={targetRef}
-        placement="bottom-left"
-        onClose={handleCloseList}
-        fullWidth
-        title={isMobile ? "Time zone" : undefined}
-      >
-        <div
-          className={classNames({
-            "vm-timezones-list": true,
-            "vm-timezones-list_mobile": isMobile,
-          })}
-        >
-          <div className="vm-timezones-list-header">
-            <div className="vm-timezones-list-header__search">
-              <TextField
-                autofocus
-                label="Search"
-                value={search}
-                onChange={handleChangeSearch}
-              />
-            </div>
-            {pinnedTimezones.map((t, i) => t && (
-              <div
-                key={`${i}_${t.region}`}
-                className="vm-timezones-item vm-timezones-list-group-options__item"
-                onClick={createHandlerSetTimezone(t)}
-              >
-                <div className="vm-timezones-item__title">{t.title}{t.isInvalid && <WarningTimezone/>}</div>
-                <div className="vm-timezones-item__utc">{t.utc}</div>
-              </div>
-            ))}
-          </div>
-          {timezonesGroups.map(t => (
-            <div
-              className="vm-timezones-list-group"
-              key={t}
-            >
-              <Accordion
-                defaultExpanded={true}
-                title={<div className="vm-timezones-list-group__title">{t}</div>}
-              >
-                <div className="vm-timezones-list-group-options">
-                  {searchTimezones[t] && searchTimezones[t].map(item => (
-                    <div
-                      className="vm-timezones-item vm-timezones-list-group-options__item"
-                      onClick={createHandlerSetTimezone(item)}
-                      key={item.search}
-                    >
-                      <div className="vm-timezones-item__title">{item.region}</div>
-                      <div className="vm-timezones-item__utc">{item.utc}</div>
-                    </div>
-                  ))}
-                </div>
-              </Accordion>
-            </div>
-          ))}
-        </div>
-      </Popper>
-    </div>
-  );
-});
-
-export default Timezones;
--- a/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/Timezones/TimezonesList.tsx
+++ b/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/Timezones/TimezonesList.tsx
@@ -0,0 +1,129 @@
+import { FC, useMemo, useState } from "preact/compat";
+import { getBrowserTimezone, getTimezoneList, getUTCByTimezone } from "../../../../utils/time";
+import classNames from "classnames";
+import Accordion from "../../../Main/Accordion/Accordion";
+import TextField from "../../../Main/TextField/TextField";
+import { Timezone } from "../../../../types";
+import "./style.scss";
+import useDeviceDetect from "../../../../hooks/useDeviceDetect";
+import WarningTimezone from "./WarningTimezone";
+import { useTimeState } from "../../../../state/time/TimeStateContext";
+
+interface PinnedTimezone extends Timezone {
+  title: string;
+  isInvalid?: boolean;
+}
+
+type Props = {
+  onChange: (tz: Timezone) => void;
+}
+
+const browserTimezone = getBrowserTimezone();
+
+const TimezonesList: FC<Props> = ({ onChange }) => {
+  const { isMobile } = useDeviceDetect();
+  const { defaultTimezone } = useTimeState();
+
+  const timezones = useMemo(() => getTimezoneList(), []);
+
+  const [search, setSearch] = useState("");
+
+  const pinnedTimezones = useMemo(() => [
+    {
+      title: `Default time (${defaultTimezone})`,
+      region: defaultTimezone,
+      utc: defaultTimezone ? getUTCByTimezone(defaultTimezone) : "UTC"
+    },
+    {
+      title: browserTimezone.title,
+      region: browserTimezone.region,
+      utc: getUTCByTimezone(browserTimezone.region),
+      isInvalid: !browserTimezone.isValid
+    },
+    {
+      title: "UTC (Coordinated Universal Time)",
+      region: "UTC",
+      utc: "UTC"
+    },
+  ].filter(t => t.region) as PinnedTimezone[], [defaultTimezone]);
+
+  const searchTimezones = useMemo(() => {
+    if (!search) return timezones;
+    try {
+      return getTimezoneList(search);
+    } catch (e) {
+      return {};
+    }
+  }, [search, timezones]);
+
+  const timezonesGroups = useMemo(() => Object.keys(searchTimezones), [searchTimezones]);
+
+  const handleChangeSearch = (val: string) => {
+    setSearch(val);
+  };
+
+  const handleSetTimezone = (tz: Timezone) => {
+    onChange(tz);
+    setSearch("");
+  };
+
+  const createHandlerSetTimezone = (val: Timezone) => () => {
+    handleSetTimezone(val);
+  };
+
+  return (
+    <div
+      className={classNames({
+        "vm-list": true,
+        "vm-timezones-list": true,
+        "vm-timezones-list_mobile": isMobile,
+      })}
+    >
+      <div className="vm-timezones-list-header">
+        <div className="vm-timezones-list-header__search">
+          <TextField
+            label="Search"
+            value={search}
+            onChange={handleChangeSearch}
+          />
+        </div>
+      </div>
+      {pinnedTimezones.map((t, i) => t && (
+        <div
+          key={`${i}_${t.region}`}
+          className="vm-list-item vm-timezones-item vm-timezones-list-group-options__item"
+          onClick={createHandlerSetTimezone(t)}
+        >
+          <div className="vm-timezones-item__title">{t.title}{t.isInvalid && <WarningTimezone/>}</div>
+          <div className="vm-timezones-item__utc">{t.utc}</div>
+        </div>
+      ))}
+      {timezonesGroups.map(t => (
+        <div
+          className="vm-timezones-list-group"
+          key={t}
+        >
+          <Accordion
+            defaultExpanded={true}
+            title={<div className="vm-timezones-list-group__title">{t}</div>}
+          >
+            <div className="vm-timezones-list-group-options">
+              {searchTimezones[t] && searchTimezones[t].map(item => (
+                <div
+                  className="vm-list-item vm-timezones-item vm-timezones-list-group-options__item"
+                  onClick={createHandlerSetTimezone(item)}
+                  key={item.search}
+                >
+                  <div className="vm-timezones-item__title">{item.region}</div>
+                  <div className="vm-timezones-item__utc">{item.utc}</div>
+                </div>
+              ))}
+            </div>
+          </Accordion>
+        </div>
+      ))}
+    </div>
+  );
+};
+
+export default TimezonesList;
--- a/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/Timezones/TimezonesPicker.tsx
+++ b/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/Timezones/TimezonesPicker.tsx
@@ -0,0 +1,71 @@
+import { FC, useMemo, useRef } from "preact/compat";
+import { getUTCByTimezone } from "../../../../utils/time";
+import { ArrowDropDownIcon } from "../../../Main/Icons";
+import classNames from "classnames";
+import { Timezone } from "../../../../types";
+import "./style.scss";
+import useBoolean from "../../../../hooks/useBoolean";
+import { useTimeDispatch, useTimeState } from "../../../../state/time/TimeStateContext";
+import TimezonesList from "./TimezonesList";
+import Popper from "../../../Main/Popper/Popper";
+import useDeviceDetect from "../../../../hooks/useDeviceDetect";
+
+const TimezonesPicker: FC = () => {
+  const { isMobile } = useDeviceDetect();
+  const { timezone: stateTimezone } = useTimeState();
+  const timeDispatch = useTimeDispatch();
+
+  const triggerRef = useRef<HTMLDivElement>(null);
+
+  const {
+    value: isOpenList,
+    toggle: toggleOpenList,
+    setFalse: handleCloseList,
+  } = useBoolean(false);
+
+  const activeTimezone = useMemo(() => ({
+    region: stateTimezone,
+    utc: getUTCByTimezone(stateTimezone)
+  }), [stateTimezone]);
+
+  const handleSetTimezone = (tz: Timezone) => {
+    timeDispatch({ type: "SET_TIMEZONE", payload: tz.region });
+    handleCloseList();
+  };
+
+  return (
+    <div className="vm-timezones">
+      <div className="vm-server-configurator__title">
+        Time zone
+      </div>
+      <div
+        className="vm-timezones-item vm-timezones-item_selected"
+        onClick={toggleOpenList}
+        ref={triggerRef}
+      >
+        <div className="vm-timezones-item__title">{activeTimezone.region}</div>
+        <div className="vm-timezones-item__utc">{activeTimezone.utc}</div>
+        <div
+          className={classNames({
+            "vm-timezones-item__icon": true,
+            "vm-timezones-item__icon_open": isOpenList
+          })}
+        >
+          <ArrowDropDownIcon/>
+        </div>
+      </div>
+      <Popper
+        open={isOpenList}
+        buttonRef={triggerRef}
+        placement="bottom-left"
+        onClose={handleCloseList}
+        fullWidth
+        title={isMobile ? "Time zone" : undefined}
+      >
+        <TimezonesList onChange={handleSetTimezone}/>
+      </Popper>
+    </div>
+  );
+};
+
+export default TimezonesPicker;
--- a/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/Timezones/style.scss
+++ b/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/Timezones/style.scss
@@ -16,6 +16,7 @@
    }

    &__title {
+      flex-grow: 1;
      display: flex;
      align-items: center;
      gap: $padding-small;
@@ -34,6 +35,7 @@
      background-color: $color-hover-black;
      padding: calc($padding-small/2);
      border-radius: $border-radius-small;
+      font-size: $font-size-small;
    }

    &__icon {
@@ -54,9 +56,11 @@
  }

  &-list {
+    padding-top: 0;
    max-height: 300px;
    background-color: $color-background-block;
    border-radius: $border-radius-medium;
+    font-size: $font-size-small;
    overflow: auto;

    &_mobile {
@@ -72,10 +76,9 @@
      top: 0;
      background-color: $color-background-block;
      z-index: 2;
-      border-bottom: $border-divider;

      &__search {
-        padding: $padding-small;
+        padding: $padding-small $padding-small calc($padding-small / 2);
      }
    }

@@ -91,6 +94,7 @@
        font-weight: bold;
        color: $color-text-secondary;
        padding: $padding-small $padding-global;
+        font-size: $font-size-small;
      }

      &-options {
@@ -98,7 +102,7 @@
        align-items: flex-start;

        &__item {
-          padding: $padding-small $padding-global;
+          padding: calc($padding-small / 2) $padding-global;
          transition: background-color 200ms ease;

          &:hover {
--- a/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/style.scss
+++ b/app/vmui/packages/vmui/src/components/Configurators/GlobalSettings/style.scss
@@ -4,9 +4,9 @@
  display: flex;
  flex-direction: column;
  align-items: center;
-  gap: $padding-large;
+  gap: calc($padding-global * 2);
  width: 600px;
-  padding-bottom: $padding-medium;
+  padding-inline: $padding-large;

  &_mobile {
    grid-auto-rows: min-content;
@@ -62,6 +62,7 @@
    justify-content: flex-end;
    gap: $padding-small;
    width: 100%;
+    padding-block: $padding-global;
  }

  &_mobile &-footer {
--- a/app/vmui/packages/vmui/src/components/Configurators/StepConfigurator/StepConfigurator.tsx
+++ b/app/vmui/packages/vmui/src/components/Configurators/StepConfigurator/StepConfigurator.tsx
@@ -22,12 +22,10 @@ const StepConfigurator: FC = () => {
  const { isMobile } = useDeviceDetect();

  const { customStep: value, isHistogram } = useGraphState();
-  const { period: { step, end, start } } = useTimeState();
+  const { period: { end, start } } = useTimeState();
  const graphDispatch = useGraphDispatch();
  const { displayType } = useCustomPanelState();

-  const prevDuration = usePrevious(end - start);
-
  const defaultStep = useMemo(() => {
    return getStepFromDuration(end - start, isHistogram, displayType);
  }, [end, start, isHistogram, displayType]);
@@ -106,16 +104,14 @@ const StepConfigurator: FC = () => {
  }, [defaultStep]);

  useEffect(() => {
-    const dur = end - start;
-    if (dur === prevDuration || !prevDuration || value !== prevDefaultStep) return;
-    if (defaultStep) {
-      handleApply(defaultStep);
-    }
-  }, [prevDuration, defaultStep]);
+    if (!prevDefaultStep) return;
+    if (value !== prevDefaultStep) return;
+    if (value === defaultStep) return;

-  useEffect(() => {
-    if (step === value || step === defaultStep) handleApply(defaultStep);
-  }, [isHistogram, displayType]);
+    graphDispatch({ type: "SET_CUSTOM_STEP", payload: defaultStep });
+    setCustomStep(defaultStep);
+    setError("");
+  }, [defaultStep, prevDefaultStep, value, graphDispatch]);

  return (
    <div
--- a/app/vmui/packages/vmui/src/components/Configurators/ThemeControl/ThemeControl.tsx
+++ b/app/vmui/packages/vmui/src/components/Configurators/ThemeControl/ThemeControl.tsx
@@ -5,8 +5,20 @@ import useDeviceDetect from "../../../hooks/useDeviceDetect";
 import classNames from "classnames";
 import { FC } from "preact/compat";
 import { useAppDispatch, useAppState } from "../../../state/common/StateContext";
+import { DarkIcon, LightIcon, SystemIcon } from "../../Main/Icons";
+
+const themeIcons = {
+  [Theme.system]: <SystemIcon/>,
+  [Theme.light]: <LightIcon/>,
+  [Theme.dark]: <DarkIcon/>,
+};
+
+const options = Object.values(Theme).map(value => ({
+  title: value,
+  value,
+  icon: themeIcons[value],
+}));

-const options = Object.values(Theme).map(value => ({ title: value, value }));
 const ThemeControl: FC = () => {
  const { isMobile } = useDeviceDetect();
  const dispatch = useAppDispatch();
@@ -25,13 +37,14 @@ const ThemeControl: FC = () => {
      })}
    >
      <div className="vm-server-configurator__title">
-        Theme preferences
+        Theme
      </div>
      <div
        className="vm-theme-control__toggle"
        key={`${isMobile}`}
      >
        <Toggle
+          size="large"
          options={options}
          value={theme}
          onChange={handleClickItem}
--- a/app/vmui/packages/vmui/src/components/Configurators/ThemeControl/style.scss
+++ b/app/vmui/packages/vmui/src/components/Configurators/ThemeControl/style.scss
@@ -4,7 +4,7 @@

  &__toggle {
    display: inline-flex;
-    min-width: 300px;
+    width: 100%;
    text-transform: capitalize;
  }

--- a/app/vmui/packages/vmui/src/components/Main/Icons/index.tsx
+++ b/app/vmui/packages/vmui/src/components/Main/Icons/index.tsx
@@ -633,3 +633,60 @@ export const DebugIcon = () => (
    />
  </svg>
 );
+
+export const SystemIcon = () => (
+  <svg
+    viewBox="0 0 24 24"
+    fill="currentColor"
+  >
+    <path
+      d="M18 4C18.7957 4 19.5595 4.3163 20.1221 4.87891C20.6845 5.44148 21 6.20452 21 7V15.5264L21.0069 15.6426C21.0203 15.7579 21.0542 15.8702 21.1065 15.9746L22.1729 18.0996C22.3271 18.4056 22.4009 18.7466 22.3858 19.0889C22.3705 19.431 22.2675 19.7637 22.0869 20.0547C21.9063 20.3457 21.6534 20.5855 21.3535 20.751C21.0555 20.9154 20.7202 21.0003 20.3799 20.999L3.62013 21C3.28002 21.0012 2.94535 20.9153 2.64748 20.751C2.34748 20.5855 2.09477 20.3458 1.91408 20.0547C1.73343 19.7636 1.63047 19.4311 1.61525 19.0889C1.60006 18.7466 1.67297 18.4056 1.82716 18.0996L2.89455 15.9746L2.94045 15.8682C2.9801 15.7589 3.00007 15.6432 3.00002 15.5264V7C3.00002 6.20442 3.3164 5.4415 3.87892 4.87891C4.44146 4.31636 5.20447 4.00007 6.00002 4H18ZM4.62404 16.9873L3.61427 18.999L3.6133 19H20.3877L20.3867 18.999L19.376 16.9873H4.62404ZM6.00002 6C5.7349 6.00007 5.48045 6.1055 5.29298 6.29297C5.10554 6.48049 5.00002 6.73485 5.00002 7V14.9873H19V7C19 6.73478 18.8946 6.48051 18.707 6.29297C18.5195 6.10552 18.2652 6 18 6H6.00002Z"
+    />
+  </svg>
+);
+
+export const LightIcon = () => (
+  <svg
+    viewBox="0 0 24 24"
+    fill="currentColor"
+  >
+    <path
+      d="M12 19C12.5523 19 13 19.4477 13 20V22C13 22.5523 12.5523 23 12 23C11.4477 23 11 22.5523 11 22V20C11 19.4477 11.4477 19 12 19Z"
+    />
+    <path
+      d="M5.63281 16.9531C6.02334 16.5627 6.65638 16.5626 7.04688 16.9531C7.43717 17.3436 7.43725 17.9767 7.04688 18.3672L5.63672 19.7773C5.24625 20.1676 4.61313 20.1676 4.22266 19.7773C3.8322 19.3869 3.83233 18.7538 4.22266 18.3633L5.63281 16.9531Z"
+    />
+    <path
+      d="M16.9531 16.9531C17.3436 16.5626 17.9767 16.5626 18.3672 16.9531L19.7773 18.3633C20.1676 18.7538 20.1678 19.3869 19.7773 19.7773C19.3869 20.1677 18.7538 20.1675 18.3633 19.7773L16.9531 18.3672C16.5626 17.9767 16.5627 17.3437 16.9531 16.9531Z"
+    />
+    <path
+      d="M12 7C14.7614 7 17 9.23858 17 12C17 14.7614 14.7614 17 12 17C9.23858 17 7 14.7614 7 12C7 9.23858 9.23858 7 12 7ZM12 9C10.3431 9 9 10.3431 9 12C9 13.6569 10.3431 15 12 15C13.6569 15 15 13.6569 15 12C15 10.3431 13.6569 9 12 9Z"
+    />
+    <path
+      d="M4 11C4.55228 11 5 11.4477 5 12C5 12.5523 4.55228 13 4 13H2C1.44772 13 1 12.5523 1 12C1 11.4477 1.44772 11 2 11H4Z"
+    />
+    <path
+      d="M22 11C22.5523 11 23 11.4477 23 12C23 12.5523 22.5523 13 22 13H20C19.4477 13 19 12.5523 19 12C19 11.4477 19.4477 11 20 11H22Z"
+    />
+    <path
+      d="M4.22266 4.22266C4.61315 3.83229 5.24623 3.83229 5.63672 4.22266L7.04688 5.63281C7.4372 6.02331 7.43723 6.65639 7.04688 7.04688C6.6564 7.43735 6.02335 7.43724 5.63281 7.04688L4.22266 5.63672C3.83225 5.24618 3.83217 4.61314 4.22266 4.22266Z"
+    />
+    <path
+      d="M18.3633 4.22266C18.7538 3.83237 19.3869 3.83232 19.7773 4.22266C20.1677 4.61312 20.1676 5.2462 19.7773 5.63672L18.3672 7.04688C17.9767 7.4373 17.3436 7.4373 16.9531 7.04688C16.5627 6.65637 16.5627 6.0233 16.9531 5.63281L18.3633 4.22266Z"
+    />
+    <path
+      d="M12 1C12.5523 1 13 1.44772 13 2V4C13 4.55228 12.5523 5 12 5C11.4477 5 11 4.55228 11 4V2C11 1.44772 11.4477 1 12 1Z"
+    />
+  </svg>
+);
+
+export const DarkIcon = () => (
+  <svg
+    viewBox="0 0 24 24"
+    fill="currentColor"
+  >
+    <path
+      d="M11.5809 2.01318C12.1851 2.02907 12.6373 2.40742 12.8475 2.84717C13.0627 3.29758 13.0619 3.86893 12.7615 4.34814L12.7606 4.34717C12.1616 5.30581 11.9061 6.43987 12.034 7.56299C12.162 8.68628 12.6672 9.73328 13.4666 10.5327C14.2661 11.332 15.3131 11.8364 16.4363 11.9644C17.5596 12.0922 18.6934 11.836 19.6522 11.2368L19.8348 11.1382C20.2701 10.9405 20.7563 10.9617 21.1512 11.1499C21.6217 11.3742 22.0194 11.8752 21.9832 12.5405C21.8789 14.4693 21.2181 16.3268 20.0809 17.8882C18.9435 19.4496 17.378 20.6484 15.574 21.3394C13.7701 22.0302 11.8042 22.184 9.91485 21.7817C8.02549 21.3794 6.29356 20.4376 4.92754 19.0718C3.56149 17.7059 2.62012 15.9739 2.21758 14.0845C1.81507 12.195 1.96826 10.2294 2.65899 8.42529C3.34975 6.62115 4.5487 5.05596 6.11016 3.91846C7.6716 2.781 9.52882 2.11947 11.4578 2.01514L11.5809 2.01318ZM10.6209 4.12061C9.42038 4.33038 8.27873 4.81214 7.28692 5.53467C6.03798 6.44459 5.07973 7.69705 4.52715 9.14014C3.97456 10.5834 3.85165 12.156 4.17364 13.6675C4.49565 15.179 5.24872 16.5649 6.34161 17.6577C7.43448 18.7505 8.82026 19.5038 10.3318 19.8257C11.8434 20.1475 13.416 20.0239 14.8592 19.4712C16.3024 18.9184 17.5548 17.9597 18.4647 16.7104C19.1869 15.7188 19.6671 14.5776 19.8768 13.3774C18.7333 13.8927 17.4674 14.0949 16.2098 13.9517C14.637 13.7725 13.1709 13.0661 12.0516 11.9468C10.9324 10.8275 10.2258 9.36126 10.0467 7.78857C9.90352 6.53075 10.1054 5.26417 10.6209 4.12061Z"
+    />
+  </svg>
+);
--- a/app/vmui/packages/vmui/src/components/Main/Switch/Switch.tsx
+++ b/app/vmui/packages/vmui/src/components/Main/Switch/Switch.tsx
@@ -1,11 +1,10 @@
-import { ReactNode } from "react";
+import { FC, ReactNode } from "preact/compat";
 import classNames from "classnames";
 import "./style.scss";
-import { FC } from "preact/compat";

 interface SwitchProps {
  value: boolean
-  color?: "primary" | "secondary" | "error"
+  color?: "primary" | "secondary" | "error" | "neutral"
  disabled?: boolean
  label?: string | ReactNode
  fullWidth?: boolean
--- a/app/vmui/packages/vmui/src/components/Main/Switch/style.scss
+++ b/app/vmui/packages/vmui/src/components/Main/Switch/style.scss
@@ -29,6 +29,10 @@ $switch-border-radius: $switch-handle-size + ($switch-padding * 2);
    background-color: $color-secondary;
  }

+  &_neutral_active &-track {
+    background-color: $color-text;
+  }
+
  &_primary_active &-track {
    background-color: $color-primary;
  }
--- a/app/vmui/packages/vmui/src/components/Main/Toggle/Toggle.tsx
+++ b/app/vmui/packages/vmui/src/components/Main/Toggle/Toggle.tsx
@@ -1,22 +1,21 @@
-import { FC, useEffect, useRef, useState } from "preact/compat";
+import { FC, useEffect, useRef, useState, ReactNode } from "preact/compat";
 import classNames from "classnames";
-import { ReactNode } from "react";
 import "./style.scss";

 interface ToggleProps {
-  options: {value: string, title?: string, icon?: ReactNode}[]
-  value: string
-  onChange: (val: string) => void
-  label?: string
+  options: { value: string, title?: string, icon?: ReactNode }[];
+  value: string;
+  onChange: (val: string) => void;
+  label?: string;
+  size?: "medium" | "large";
 }

-const Toggle: FC<ToggleProps> = ({ options, value, label, onChange }) => {
+const Toggle: FC<ToggleProps> = ({ options, value, label, size = "medium", onChange }) => {

  const activeRef = useRef<HTMLDivElement>(null);
  const [position, setPosition] = useState({
    width: "0px",
    left: "0px",
-    borderRadius: "0px"
  });

  const createHandlerChange = (value: string) => () => {
@@ -28,35 +27,25 @@ const Toggle: FC<ToggleProps> = ({ options, value, label, onChange }) => {
      setPosition({
        width: "0px",
        left: "0px",
-        borderRadius: "0px"
      });
      return;
    }
    const index = options.findIndex(o => o.value === value);
    const { width: widthRect } = activeRef.current.getBoundingClientRect();

-    let width = widthRect;
-    let left = index * width;
-    let borderRadius = "0";
-    if (index === 0) borderRadius = "16px 0 0 16px";
+    const width = widthRect;
+    const left = index * width;

-    if (index === options.length - 1) {
-      borderRadius = "10px";
-      left -= 1;
-      borderRadius = "0 16px 16px 0";
-    }
-
-    if (index !== 0 && (index !== options.length - 1)) {
-      width += 1;
-      left -= 1;
-    }
-
-
-    setPosition({ width: `${width}px`, left: `${left}px`, borderRadius });
+    setPosition({ width: `${width}px`, left: `${left}px` });
  }, [activeRef, value, options]);

  return (
-    <div className="vm-toggles">
+    <div
+      className={classNames({
+        "vm-toggles": true,
+        [`vm-toggles_${size}`]: size,
+      })}
+    >
      {label && (
        <label className="vm-toggles__label">
          {label}
@@ -66,15 +55,14 @@ const Toggle: FC<ToggleProps> = ({ options, value, label, onChange }) => {
        className="vm-toggles-group"
        style={{ gridTemplateColumns: `repeat(${options.length}, 1fr)` }}
      >
-        {position.borderRadius && <div
+        <div
          className="vm-toggles-group__highlight"
          style={position}
-        />}
-        {options.map((option, i) => (
+        />
+        {options.map((option) => (
          <div
            className={classNames({
              "vm-toggles-group-item": true,
-              "vm-toggles-group-item_first": i === 0,
              "vm-toggles-group-item_active": option.value === value,
              "vm-toggles-group-item_icon": option.icon && option.title
            })}
--- a/app/vmui/packages/vmui/src/components/Main/Toggle/style.scss
+++ b/app/vmui/packages/vmui/src/components/Main/Toggle/style.scss
@@ -20,6 +20,8 @@
    align-items: center;
    justify-content: center;
    overflow: hidden;
+    border-radius: $border-radius-small;
+    background: $color-hover-black;

    &-item {
      position: relative;
@@ -27,55 +29,68 @@
      align-items: center;
      justify-content: center;
      padding: $padding-small;
-      border-right: $border-divider;
-      border-top: $border-divider;
-      border-bottom: $border-divider;
      font-size: $font-size-small;
      color: $color-text-secondary;
-      font-weight: bold;
+      font-weight: 500;
      cursor: pointer;
      text-align: center;
-      transition: color 150ms ease-in;
+      transition: opacity 150ms ease-in, color 150ms ease-in;
      z-index: 2;
      user-select: none;

-      &_first {
-        border-radius: 16px 0 0 16px;
-        border-left: $border-divider
-      }
-
-      &:last-child {
-        border-radius: 0 16px 16px 0;
-        border-left: none;
-      }
-
      &_icon {
        grid-template-columns: 14px auto;
-        gap: 4px;
+        gap: calc($padding-small / 2);
      }

-      &:hover {
-        color: $color-primary;
+      &:hover:not(&_active) {
+        opacity: 0.8;
      }

      &_active {
-        color: $color-primary;
-        border-color: transparent;
-
-        &:hover {
-          background-color: transparent;
-        }
+        color: $color-text;
+        font-weight: 600;
      }
    }

    &__highlight {
      position: absolute;
      top: 0;
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      padding: 3px;
      height: 100%;
      background-color: rgba($color-primary, 0.08);
-      border: 1px solid $color-primary;
      transition: left 200ms cubic-bezier(0.280, 0.840, 0.420, 1), border-radius 200ms linear;
      z-index: 1;
+
+      &:after {
+        content: '';
+        height: 100%;
+        width: 100%;
+        background-color: $color-background-block;
+        border-radius: $border-radius-small;
+        box-shadow: $box-shadow;
+      }
+    }
+  }
+
+  &_large &-group {
+    border-radius: $border-radius-medium;
+
+    &-item {
+      padding: $padding-global $padding-small;
+
+      &_icon {
+        grid-template-columns: 16px auto;
+        gap: $padding-small;
+      }
+    }
+
+    &__highlight {
+      padding: 4px;
+      border-radius: $border-radius-medium;
    }
  }
 }
--- a/app/vmui/packages/vmui/src/constants/faviconColors.ts
+++ b/app/vmui/packages/vmui/src/constants/faviconColors.ts
@@ -0,0 +1,14 @@
+export const faviconColors = [
+  "#A1A1AA",
+  "#71717A",
+  "#020202",
+  "#E94600",
+  "#FF7A00",
+  "#F2B705",
+  "#84CC16",
+  "#16B86A",
+  "#00AFAF",
+  "#2979FF",
+  "#8B5CF6",
+  "#E83E9A",
+] as const;
--- a/app/vmui/packages/vmui/src/layouts/MainLayout/MainLayout.tsx
+++ b/app/vmui/packages/vmui/src/layouts/MainLayout/MainLayout.tsx
@@ -14,6 +14,9 @@ import useFetchDefaultTimezone from "../../hooks/useFetchDefaultTimezone";
 import useFetchAppConfig from "../../hooks/useFetchAppConfig";
 import WebStorageCheck from "../../components/WebStorageCheck/WebStorageCheck";
 import { migrateStorageToPrefixedKeys } from "../../utils/storage";
+import {
+  useBrowserTabSync
+} from "../../components/Configurators/GlobalSettings/BrowserTabController/hooks/useBrowserTabSync";

 const MainLayout: FC = () => {
  const appModeEnable = getAppModeEnable();
@@ -21,6 +24,7 @@ const MainLayout: FC = () => {
  const { pathname } = useLocation();
  const [searchParams, setSearchParams] = useSearchParams();

+  useBrowserTabSync();
  useFetchDashboards();
  useFetchDefaultTimezone();
  useFetchAppConfig();
--- a/app/vmui/packages/vmui/src/utils/favicon.tsx
+++ b/app/vmui/packages/vmui/src/utils/favicon.tsx
@@ -0,0 +1,29 @@
+import faviconRaw from "../../assets/favicon.svg?raw";
+
+export const createFaviconUrl = (color = "#020202"): string => {
+  const svgDocument = new DOMParser().parseFromString(faviconRaw, "image/svg+xml");
+  const svg = svgDocument.documentElement;
+
+  if (svg.localName !== "svg") {
+    throw new Error("Invalid favicon SVG");
+  }
+
+  svg.setAttribute("fill", color);
+
+  const serializedSvg = new XMLSerializer().serializeToString(svg);
+  return `data:image/svg+xml;charset=utf-8,${encodeURIComponent(serializedSvg)}`;
+};
+
+export const updateFaviconColor = (color = "#020202"): void => {
+  const favicon = document.querySelector<HTMLLinkElement>("#favicon");
+  if (favicon) {
+    favicon.href = createFaviconUrl(color);
+  }
+
+  const maskIcon = document.querySelector<HTMLLinkElement>("#mask-icon");
+  if (maskIcon) {
+    maskIcon.setAttribute("color", color);
+  }
+};
+
+export const getFaviconStorageKey = () => window.location.pathname.replace(/\/+$/, "") || "/";
--- a/app/vmui/packages/vmui/src/utils/storage.ts
+++ b/app/vmui/packages/vmui/src/utils/storage.ts
@@ -17,7 +17,11 @@ export const ALL_STORAGE_KEYS = [
  "POINTS_SHOW_ALL",
 ] as const;

-export type StorageKeys = (typeof ALL_STORAGE_KEYS)[number];
+export type FaviconStorageKey = `FAVICON_COLOR:${string}`;
+
+export type StorageKeys =
+  | (typeof ALL_STORAGE_KEYS)[number]
+  | FaviconStorageKey;

 type PrefixedStorageKeys = `${typeof STORAGE_PREFIX}${StorageKeys}`;

@@ -58,7 +62,10 @@ export const getFromStorage = (key: StorageKeys, withPrefix = true): undefined |

 export const removeFromStorage = (keys: StorageKeys[], withPrefix = true): void => {
  const storageKeys = withPrefix ? keys.map(toPrefixedKey) : keys;
-  storageKeys.forEach(k => window.localStorage.removeItem(k));
+  storageKeys.forEach(k => {
+    window.localStorage.removeItem(k);
+    window.dispatchEvent(new StorageEvent("storage", { key: k }));
+  });
 };

 /**
--- a/app/vmui/packages/vmui/src/utils/time.ts
+++ b/app/vmui/packages/vmui/src/utils/time.ts
@@ -205,19 +205,21 @@ export const getUTCByTimezone = (timezone: string) => {
 };

 export const getTimezoneList = (search = "") => {
-  const regexp = new RegExp(search, "i");
+  const normalizedSearch = search.toLowerCase();

-  return supportedTimezones.reduce((acc: {[key: string]: Timezone[]}, region) => {
+  return supportedTimezones.reduce((acc: { [key: string]: Timezone[] }, region) => {
    const zone = (region.match(/^(.*?)\//) || [])[1] || "unknown";
    const utc = getUTCByTimezone(region);
-    const utcForSearch = utc.replace(/UTC|0/, "");
+    const utcForSearch = utc.replace(/^UTC/, "");
    const regionForSearch = region.replace(/[/_]/g, " ");
+
    const item = {
      region,
      utc,
      search: `${region} ${utc} ${regionForSearch} ${utcForSearch}`
    };
-    const includeZone = !search || (search && regexp.test(item.search));
+
+    const includeZone = !normalizedSearch || item.search.toLowerCase().includes(normalizedSearch);

    if (includeZone && acc[zone]) {
      acc[zone].push(item);
--- a/app/vmui/packages/vmui/vite.config.ts
+++ b/app/vmui/packages/vmui/vite.config.ts
@@ -50,6 +50,13 @@ export default defineConfig(() => {
              return "vendor";
            }
          },
+          assetFileNames: (assetInfo) => {
+            if (assetInfo.names.includes("favicon.svg")) {
+              return "assets/favicon.svg";
+            }
+
+            return "assets/[name]-[hash][extname]";
+          },
        },
      },
    },
--- a/deployment/docker/vmanomaly/vmanomaly-integration/compose.yml
+++ b/deployment/docker/vmanomaly/vmanomaly-integration/compose.yml
@@ -59,7 +59,7 @@ services:
      - '--external.alert.source=explore?orgId=1&left=["now-1h","now","VictoriaMetrics",{"expr": },{"mode":"Metrics"},{"ui":[true,true,true,"none"]}]'
    restart: always
  vmanomaly:
-    image: victoriametrics/vmanomaly:v1.30.0
+    image: victoriametrics/vmanomaly:v1.30.1
    depends_on:
      - "victoriametrics"
    ports:
--- a/docs/ai-tools/README.md
+++ b/docs/ai-tools/README.md
@@ -1,3 +1,11 @@
+---
+build:
+  list: never
+  publishResources: false
+  render: never
+sitemap:
+  disable: true
+---
 VictoriaMetrics Observability Stack integrates with AI assistants through [MCP servers](https://docs.victoriametrics.com/ai-tools/#mcp-servers)
 and [agent skills](https://docs.victoriametrics.com/ai-tools/#agent-skills).
 The integrations allow AI agents and automation tools to query Metrics, Logs, and Traces, analyze telemetry data, 
--- a/docs/anomaly-detection/CHANGELOG.md
+++ b/docs/anomaly-detection/CHANGELOG.md
@@ -16,6 +16,27 @@ Please find the changelog for VictoriaMetrics Anomaly Detection below.

 {{% collapse name="2026" open=true %}}

+## v1.30.1
+Released: 2026-08-06
+
+- UI: Updated [vmanomaly UI](https://docs.victoriametrics.com/anomaly-detection/ui/) from [v1.8.0](https://docs.victoriametrics.com/anomaly-detection/ui/#v180) to [v1.8.1](https://docs.victoriametrics.com/anomaly-detection/ui/#v181). The update improves UX validation and fixes regressions introduced by new design.
+
+- IMPROVEMENT: Reduced fit and inference latency for the Z-score, MAD, standard deviation, Seasonal Quantile, and Rolling Quantile online models. Representative service-stage gains range from 1.5-2.6x for fit and 1.7-2.3x for inference, depending on model, storage mode, and data size.
+
+- IMPROVEMENT: Removed forwarded datasource credentials from in-memory state for completed, failed, canceled, and shutting-down [analysis and autotune tasks](https://docs.victoriametrics.com/anomaly-detection/components/server/#time-series-analysis-and-autotune-api).
+
+- BUGFIX: Stabilized [Temporal Envelope](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) after fitting across a late level shift. Its level, trend, residual, and supported calendar state now initialize coherently from the recent regime, avoiding stale fitted magnitudes and false seasonal oscillations when periodic inference starts.
+
+- BUGFIX: Corrected `/api/v1/timeseries/characteristics` seasonality detection for time series whose timestamps are offset from whole sampling intervals. Trend interpolation now preserves the original observation grid, allowing daily and weekly patterns to be detected on shifted grids.
+
+- BUGFIX: Restored backward-compatible `inference_only` [backtesting](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/#backtesting-scheduler) for configurations that omit `infer_every`. The scheduler derives its inference grid from the query step or reader sampling period and preserves valid single-timestamp range queries.
+
+- BUGFIX: Aligned periodic inference for exact-capable online models with exact backtesting (used in [UI](https://docs.victoriametrics.com/anomaly-detection/ui/) experiments) by applying the configured `infer_every` as the causal update cadence.
+
+- BUGFIX: Corrected [self-monitoring](https://docs.victoriametrics.com/anomaly-detection/components/monitoring/#writer-behaviour-metrics) accounting so failed VictoriaMetrics write attempts contribute to `vmanomaly_writer_request_duration_seconds`, including connection retries, and inference counts only unseen *valid* rows in `vmanomaly_model_datapoints_accepted`.
+
+- BUGFIX: Fixed service-level [`settings.anomaly_score_outside_data_range`](https://docs.victoriametrics.com/anomaly-detection/components/settings/#anomaly-score-outside-data-range) propagation so its configured score applies to every model unless the model defines its own override.
+
 ## v1.30.0
 Released: 2026-07-23

--- a/docs/anomaly-detection/FAQ.md
+++ b/docs/anomaly-detection/FAQ.md
@@ -24,7 +24,7 @@ The decision to set the changepoint at `1.0` is made to ensure consistency acros
 > `anomaly_score` is a metric itself, which preserves all labels found in input data and (optionally) appends [custom labels, specified in writer](https://docs.victoriametrics.com/anomaly-detection/components/writer/#metrics-formatting) - follow the link for detailed output example.

 ## How is anomaly score calculated?
-For most of the [univariate models](https://docs.victoriametrics.com/anomaly-detection/components/models/#univariate-models) that can generate `yhat`, `yhat_lower`, and `yhat_upper` time series in [their output](https://docs.victoriametrics.com/anomaly-detection/components/models/#vmanomaly-output) (such as [Prophet](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet) or [Z-score](https://docs.victoriametrics.com/anomaly-detection/components/models/#z-score)), the anomaly score is calculated as follows:
+For most of the [univariate models](https://docs.victoriametrics.com/anomaly-detection/components/models/#univariate-models) that can generate `yhat`, `yhat_lower`, and `yhat_upper` time series in [their output](https://docs.victoriametrics.com/anomaly-detection/components/models/#vmanomaly-output) (such as [Temporal Envelope](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) or [Online Z-score](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-z-score)), the anomaly score is calculated as follows:
 - If `yhat` (expected series behavior) equals `y` (actual value observed), then the anomaly score is 0.
 - If `y` (actual value observed) falls within the `[yhat_lower, yhat_upper]` confidence interval, the anomaly score will gradually approach 1, the closer `y` is to the boundary.
 - If `y` (actual value observed) strictly exceeds the `[yhat_lower, yhat_upper]` interval, the anomaly score will be greater than 1, increasing as the margin between the actual value and the expected range grows.
@@ -82,7 +82,7 @@ reader:

 `vmanomaly` supports timezone-aware anomaly detection {{% available_from "v1.18.0" anomaly %}} through a `tz` argument, available both at the [reader level](https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader) and at the [query level](https://docs.victoriametrics.com/anomaly-detection/components/reader/#per-query-parameters).

-For models that depend on seasonality, such as [`ProphetModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet) and [`OnlineQuantileModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile), handling timezone shifts is crucial. Changes like Daylight Saving Time (DST) can disrupt seasonality patterns learned by models, resulting in inaccurate anomaly predictions as the periodic patterns shift with time. Proper timezone configuration ensures that seasonal cycles align with expected intervals, even as DST changes occur.
+For models that depend on seasonality, such as [`TemporalEnvelopeModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) and [`OnlineQuantileModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile), handling timezone shifts is crucial. Changes like Daylight Saving Time (DST) can disrupt seasonality patterns learned by models, resulting in inaccurate anomaly predictions as the periodic patterns shift with time. Proper timezone configuration ensures that seasonal cycles align with expected intervals, even as DST changes occur.

 To enable timezone handling:
 1. **Reader-level**: Set `tz` in the [`reader`](https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader) section to a specific timezone (e.g., `Europe/Berlin`) to apply this setting to all queries.
@@ -100,9 +100,9 @@ reader:
      tz: 'Europe/London'  # per-query override
 models:
  seasonal_model:
-    class: 'prophet'
+    class: 'temporal_envelope'
    queries: ['your_query']
-    # other model params ...
+    seasonalities: ['hod_smooth', 'dow_smooth']
 ```

 ## Output produced by vmanomaly
@@ -124,9 +124,8 @@ Selecting the best model for `vmanomaly` depends on the data's nature and the [t

 - Use [Online MAD](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-mad) for simple, mostly stationary data with no-to-slow trend, when robustness to outliers is important.
 - Use [Online Z-score](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-z-score) for simple, light-tailed data where standard-deviation units are meaningful.
- Use [Temporal Envelope](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) {{% available_from "v1.30.0" anomaly %}} for complex data with trends, calendar patterns, holidays, or persistent shifts. It is the preferred *online* alternative to Prophet (which will be deprecated in the future releases).
+- Use [Temporal Envelope](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) {{% available_from "v1.30.0" anomaly %}} for complex data with trends, calendar patterns, holidays, or persistent shifts. It is the preferred online migration target for existing Prophet configurations.
 - Use multivariate Temporal Envelope when normal relationships between aligned metrics matter. This should replace [Isolation Forest](https://docs.victoriametrics.com/anomaly-detection/components/models/#isolation-forest-multivariate) used in previous versions of `vmanomaly`, which will be deprecated in future releases.
- Use [Prophet](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet) when Prophet-specific decomposition outputs, or offline batch behavior are required. Consider using Temporal Envelope instead, as it is more efficient and provides better results in most cases.

 There is also an option to auto-tune the most important parameters of a selected model class {{% available_from "v1.12.0" anomaly %}}. {{% available_from "v1.30.0" anomaly %}} The asynchronous autotune API can first profile a bounded sample through `/api/v1/timeseries/characteristics`, then tune a shared concrete configuration through `/api/v1/autotune/tasks`. See the [autotune workflow](https://docs.victoriametrics.com/anomaly-detection/components/models/#shared-asynchronous-autotune-workflow).

@@ -254,7 +253,7 @@ Configuration above will produce N intervals of full length (`fit_window`=14d +

 ## Forecasting

-`vmanomaly` can generate future forecasts using [Temporal Envelope](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) {{% available_from "v1.30.0" anomaly %}} or [ProphetModel](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet) {{% available_from "v1.25.3" anomaly %}}. This is helpful for capacity planning, resource allocation, or trend analysis when the underlying data is complex and exceeds what inline MetricsQL queries, including [predict_linear](https://docs.victoriametrics.com/victoriametrics/metricsql/#predict_linear), can handle.
+`vmanomaly` can generate future forecasts with [Temporal Envelope](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) {{% available_from "v1.30.0" anomaly %}}, the preferred online forecasting model. [ProphetModel](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet) {{% available_from "v1.25.3" anomaly %}} also supports forecasting for existing offline configurations. Forecasts help with capacity planning, resource allocation, or trend analysis when the underlying data is complex and exceeds what inline MetricsQL queries, including [predict_linear](https://docs.victoriametrics.com/victoriametrics/metricsql/#predict_linear), can handle.

 > However, please note that this mode should be used with care, as the model will produce `yhat_{h}` (and probably `yhat_lower_{h}`, and `yhat_upper_{h}`) time series **for each timeseries returned by input queries and for each forecasting horizon specified in `forecast_at` argument, which can lead to a significant increase in the number of active timeseries in VictoriaMetrics TSDB**.

@@ -432,7 +431,7 @@ services:
  # ...
  vmanomaly:
    container_name: vmanomaly
-    image: victoriametrics/vmanomaly:v1.30.0
+    image: victoriametrics/vmanomaly:v1.30.1
    # ...
    restart: always
    volumes:
@@ -554,7 +553,8 @@ reader:
      expr: 'sum(ALERTS{alertstate=~'(pending|firing)'}) by (alertstate)'
      max_points_per_query: 5000  # query-level override
 models:
-    prophet:
+    temporal_envelope:
+      class: temporal_envelope
      # other model args
      queries: [
        'sum_alerts',
@@ -575,7 +575,8 @@ reader:
    sum_alerts:
      expr: 'sum(ALERTS{alertstate=~'(pending|firing)'}) by (alertstate)'
 models:
-    prophet:
+    temporal_envelope:
+      class: temporal_envelope
      # other model args
      queries: [
        'sum_alerts',
@@ -594,7 +595,8 @@ reader:
    sum_alerts_firing:
      expr: 'sum(ALERTS{alertstate='firing'}) by ()'
 models:
-    prophet:
+    temporal_envelope:
+      class: temporal_envelope
      # other model args
      queries: [
        'sum_alerts_pending',
@@ -652,7 +654,7 @@ options:
 Here’s an example of using the config splitter to divide configurations based on the `extra_filters` argument from the reader section:

 ```sh
-docker pull victoriametrics/vmanomaly:v1.30.0 && docker image tag victoriametrics/vmanomaly:v1.30.0 vmanomaly
+docker pull victoriametrics/vmanomaly:v1.30.1 && docker image tag victoriametrics/vmanomaly:v1.30.1 vmanomaly
 ```

 ```sh
--- a/docs/anomaly-detection/Migration.md
+++ b/docs/anomaly-detection/Migration.md
@@ -45,7 +45,7 @@ There are 2 types of compatibility to consider when migrating in stateful mode:

 | Group start | Group end | Compatibility | Notes |
 |---------|--------- |------------|-------|
-| [v1.29.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1291) | [v1.30.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1300) | Fully Compatible | v1.30.0 adds new [Temporal Envelope](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) model state without changing the compatibility of existing model and data artifacts. |
+| [v1.29.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1291) | [v1.30.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1301) | Fully Compatible | v1.30.0 adds new [Temporal Envelope](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) model state without changing the compatibility of existing model and data artifacts. v1.30.1 remains compatible with v1.30.0 state and its compatible predecessors. |
 | [v1.28.7](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1287) | [v1.29.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1290) | Partially compatible* | Dumped models of class [prophet](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet) and [seasonal quantile](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile) have problems with loading to [v1.29.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1290) due to dropped `pytz` library. **Upgrading directly from v1.28.7 to [v1.29.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1291) with a fix is suggested** |
 | [v1.26.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1262) | [v1.28.7](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1287) | Fully Compatible | [v1.28.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1280) introduced [rolling](https://docs.victoriametrics.com/anomaly-detection/components/models/#rolling-models) model class drop in favor of [online](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-models) models (`rolling_quantile` and `std` models), however, it does not impact compatibility, as artifacts were not produced by default for rolling models. Also, offline `mad` and `zscore` models are redirecting to their respective online counterparts since [v1.28.4](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1284). |
 | [v1.25.3](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1253) | [v1.26.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1270) | Partially Compatible* | [v1.25.3](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1253) introduced `forecast_at` argument for base [univariate](https://docs.victoriametrics.com/anomaly-detection/components/models/#univariate-models) and `Prophet` [models](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet), however, itself remains backward-reversible from newer states like [v1.26.2](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1262), [v1.27.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1270). (All models except `isolation_forest_multivariate` class will be dropped) |
@@ -71,7 +71,7 @@ In stateless mode, the migration process is almost straightforward as there are

 **Breaking Changes**

- [v1.12.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1120) **ARIMA** model is removed from [built-in models](https://docs.victoriametrics.com/anomaly-detection/components/models/#built-in-models); Action:  replace ARIMA by [Prophet](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet) or alternative seasonal models in `model(s)` section of your configuration files.
+- [v1.12.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1120) **ARIMA** model is removed from [built-in models](https://docs.victoriametrics.com/anomaly-detection/components/models/#built-in-models). Action: for vmanomaly v1.30.0 and newer, replace ARIMA with [Temporal Envelope](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) {{% available_from "v1.30.0" anomaly %}}; for older releases, use another supported seasonal model in the `models` section of the configuration.

 - [v1.9.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v190) The `sampling_period` parameter is now mandatory in `VmReader`. This change aims to clarify and standardize the frequency of input/output in `vmanomaly`, thereby reducing uncertainty and aligning with user expectations; Action: Add the `sampling_period` parameter to your `VmReader` configuration, e.g.:

--- a/docs/anomaly-detection/QuickStart.md
+++ b/docs/anomaly-detection/QuickStart.md
@@ -126,13 +126,18 @@ groups:
 > docker pull quay.io/victoriametrics/vmanomaly:vX.Y.Z
 > ```

+> [!NOTE] ARM64 startup on affected Apple Silicon virtualization
+> On some `linux/arm64` environments running through virtualization on Apple M4/M5 hosts, `vmanomaly` may exit with `SIGILL` (exit code `132`) before startup. This is caused by the virtualized host advertising an SVE2 capability that traps when used by OpenSSL 4.x; it does not affect all ARM64 systems.
+>
+> On affected hosts, add `-e OPENSSL_armcap=0` to `docker run`, or add `- OPENSSL_armcap=0` under the service's Docker Compose `environment`, matching the list syntax used below. This disables ARM cryptographic acceleration, so apply it only as a temporary workaround on affected hosts.
+

 Below are the steps to get `vmanomaly` up and running inside a Docker container:

 1. Pull Docker image:

 ```sh
-docker pull victoriametrics/vmanomaly:v1.30.0
+docker pull victoriametrics/vmanomaly:v1.30.1
 ```

 2. Create the license file with your license key.
@@ -152,7 +157,7 @@ docker run -it \
    -v ./license:/license \
    -v ./config.yaml:/config.yaml \
    -p 8490:8490 \
-    victoriametrics/vmanomaly:v1.30.0 \
+    victoriametrics/vmanomaly:v1.30.1 \
    /config.yaml \
    --licenseFile=/license \
    --loggerLevel=INFO \
@@ -169,7 +174,7 @@ docker run -it \
    -e VMANOMALY_DATA_DUMPS_DIR=/tmp/vmanomaly/data \
    -e VMANOMALY_MODEL_DUMPS_DIR=/tmp/vmanomaly/models \
    -p 8490:8490 \
-    victoriametrics/vmanomaly:v1.30.0 \
+    victoriametrics/vmanomaly:v1.30.1 \
    /config.yaml \
    --licenseFile=/license \
    --loggerLevel=INFO \
@@ -182,7 +187,7 @@ services:
  # ...
  vmanomaly:
    container_name: vmanomaly
-    image: victoriametrics/vmanomaly:v1.30.0
+    image: victoriametrics/vmanomaly:v1.30.1
    # ...
    restart: always
    volumes:
--- a/docs/anomaly-detection/UI.md
+++ b/docs/anomaly-detection/UI.md
@@ -193,7 +193,7 @@ The best applications of this mode are:

 ### What you can do with Copilot

- **Ask questions** about any model (e.g. [Temporal Envelope](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope), [Prophet](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet), or [Z-score](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-z-score) - parameters, trade-offs, when to use each)
+- **Ask questions** about any model (e.g. [Temporal Envelope](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope), [Online Seasonal Quantile](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile), or [Online Z-score](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-z-score) - parameters, trade-offs, when to use each)
 - **Improve detection quality** - describe what's wrong ("too many false positives", "missing spikes") and Copilot reads the config, searches the docs, and proposes a validated configuration change to fix the issue.
 - **Get config suggestions inline** - suggestions appear as interactive cards with an explanation and a YAML diff; click **Apply** to write the change directly to your current settings, or **Decline** to keep the conversation going.
 - {{% available_from "v1.30.0" anomaly %}} **Profile and tune the real query** - with [mcp-vmanomaly](#mcp-tools-server) connected, Copilot can inspect bounded time-series characteristics, recommend an online model, start an asynchronous autotune task, and apply its validated query and model suggestions.
@@ -316,7 +316,7 @@ docker run -it --rm \
  -e VMANOMALY_MCP_SERVER_URL=http://mcp-vmanomaly:8081/mcp \
  -p 8080:8080 \
  -p 8490:8490 \
-  victoriametrics/vmanomaly:v1.30.0 \
+  victoriametrics/vmanomaly:v1.30.1 \
  vmanomaly_config.yaml
 ```

@@ -569,7 +569,7 @@ Set up the time range and resolution (step) for data visualization and anomaly d

 ![vmanomaly-ui-sections-explore](vmanomaly-ui-sections-explore.webp)

-Pay attention to trends, seasonality, noise, outliers, and other patterns in the data, which can influence the choice of anomaly detection model and its hyperparameters (e.g. use seasonal models for seasonal data - like `Prophet`, robust models for noisy de-seasonalized data - like `MAD`, etc.).
+Pay attention to trends, seasonality, noise, outliers, and other patterns in the data, which can influence the choice of anomaly detection model and its hyperparameters. Use [Temporal Envelope](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) for complex data with trend or calendar patterns, and [Online MAD](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-mad) for simple, mostly stationary data where robustness to outliers matters.

 ![vmanomaly-ui-sections-plot-area-query-mode](vmanomaly-ui-sections-plot-area-query-mode.webp)

@@ -645,6 +645,19 @@ If the **results** look good and the **model configuration should be deployed in

 {{% collapse name="Release history" %}}

+### v1.8.1
+Released: 2026-08-06
+
+vmanomaly version: [v1.30.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1301)
+
+- IMPROVEMENT: Model settings are validated and normalized when applied. Invalid drafts remain open with actionable feedback, and advanced-setting summaries open the corresponding editor directly.
+
+- BUGFIX: Server-query counts load when the query drawer opens, and numeric model fields preserve valid scalar and range values while reporting parsing errors on blur.
+
+- BUGFIX: The anomaly visualization empty state now follows the active theme instead of using light-theme colors in dark mode.
+
+- BUGFIX: Tenant selection now follows the datasource URL resolved by the server, avoiding an incorrect switch to tenant `0` when it is unavailable.
+
 ### v1.8.0
 Released: 2026-07-23

--- a/docs/anomaly-detection/components/README.md
+++ b/docs/anomaly-detection/components/README.md
@@ -54,11 +54,11 @@ schedulers:
    fit_window: "3d"  # how much historical data to use for fit stage
    start_from: "00:00"  # align the annual fit schedule to midnight in the configured timezone
    tz: "Europe/Kyiv"  # timezone to use for start_from
-  periodic_offline_1w:
+  periodic_online_weekly:
    class: 'periodic'
    infer_every: "15m"
    scatter_infer_jobs: true
-    fit_every: "24h"
+    fit_every: "365d"  # online state continues adapting between infrequent full re-fits
    fit_window: "14d"
    # if no start_from is specified, jobs will start immediately after service starts

@@ -75,18 +75,16 @@ models:
    min_dev_from_expected: 0.0  # turned off. if |y - yhat| < min_dev_from_expected, anomaly score will be 0
    detection_direction: 'above_expected' # detect anomalies only when y > yhat, "peaks"
    clip_predictions: True  # clip predictions to expected data range, i.e. [0, inf] for this query `host_network_receive_errors
-  prophet_weekly: # we can set up alias for model
-    class: 'prophet'
+  envelope_weekly: # we can set up alias for model
+    class: 'temporal_envelope'
    provide_series: ['anomaly_score', 'y', 'yhat', 'yhat_lower', 'yhat_upper']
    queries: ['cpu_seconds_total']
-    schedulers: ['periodic_offline_1w']  # will be attached to 1-week scheduler, re-fit every 24h and infer every 15m
+    schedulers: ['periodic_online_weekly']  # fit on two weekly cycles, then update online every 15m
    min_dev_from_expected: [0.01, 0.01]  # minimum deviation from expected value to be even considered as anomaly
    anomaly_score_outside_data_range: 1.5  # override default anomaly score outside expected data range
    detection_direction: 'above_expected'
    clip_predictions: True  # clip predictions to expected data range, i.e. [0, inf] for this query `cpu_seconds_total`
-    args:  # model-specific arguments
-      interval_width: 0.98
-      yearly_seasonality: False  # disable yearly seasonality, since we have only 7 days of data
+    seasonalities: ['hod_smooth', 'dow_smooth']

 # where to read data from
 # https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader
@@ -113,7 +111,7 @@ reader:
 # https://docs.victoriametrics.com/anomaly-detection/components/writer/
 writer:
  datasource_url: "http://victoriametrics:8428/"
-  # tenant_id: "0:0"  # for VictoriaMetrics cluster, can support "multitenant"
+  tenant_id: "0:0"  # for VictoriaMetrics cluster, can support "multitenant"
  # https://docs.victoriametrics.com/anomaly-detection/components/writer/#metrics-formatting
  metric_format:
    __name__: $VAR
@@ -204,6 +202,7 @@ models:

 writer:
  datasource_url: "http://victoriametrics:8428/"
+  tenant_id: "0:0"

 monitoring:
  push:
--- a/docs/anomaly-detection/components/models.md
+++ b/docs/anomaly-detection/components/models.md
@@ -27,16 +27,13 @@ This section covers the `Models` component of VictoriaMetrics Anomaly Detection
 ```yaml
 models:
  model_univariate_1:
-    class: 'zscore' # or 'model.zscore.ZscoreModel' until v1.13.0
+    class: 'zscore_online'
    z_threshold: 2.5
    queries: ['query_alias2']  # referencing queries defined in `reader` section
  model_multivariate_1:
-    class: 'isolation_forest_multivariate'  # or model.isolation_forest.IsolationForestMultivariateModel until v1.13.0
-    contamination: 'auto'
-    args:
-      n_estimators: 100
-      # i.e. to assure reproducibility of produced results each time model is fit on the same input
-      random_state: 42
+    class: 'temporal_envelope_multivariate'
+    seasonalities: ['hod_smooth', 'dow_smooth']
+    provide_series: ['anomaly_score']
    # if there is no explicit `queries` arg, then the model will be run on ALL queries found in reader section
 # ...
 ```  
@@ -334,9 +331,10 @@ reader:
      + sum(rate(node_network_transmit_bytes_total[5m])) by (host)

 models:
-  iforest: # alias for the model
-    class: isolation_forest_multivariate
-    contamination: 0.01
+  envelope: # alias for the model
+    class: temporal_envelope_multivariate
+    seasonalities: [hod_smooth, dow_smooth]
+    provide_series: [anomaly_score]
    # the multivariate model can be trained on 2+ timeseries returned by 1+ queries
    queries: [cpu, ram, network]
    # train a distinct multivariate model for each unique value found in the `host` label
@@ -548,7 +546,7 @@ If during an inference, you got a series having **new labelset** (not present in

 **Implications:** Univariate models are a go-to default, when your queries returns **changing** amount of **individual** time series of **different** magnitude, [trend](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#trend) or [seasonality](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#seasonality), so you won't be mixing incompatible data with different behavior within a single fit model (context isolation).

-**Examples:** [Prophet](#prophet), [Holt-Winters](#holt-winters)
+**Examples:** [Temporal Envelope](#temporal-envelope), [Online MAD](#online-mad), [Online Z-score](#online-z-score), [Online Seasonal Quantile](#online-seasonal-quantile)

 ![Univariate model lifecycle](model-lifecycle-univariate.svg)

@@ -565,11 +563,11 @@ If during an inference, you got a **different amount of series** or some series

 **Implications:** Multivariate models are a go-to default, when your queries returns **fixed** amount of **individual** time series (say, some aggregations), to be used for adding cross-series (and cross-query) context, useful for catching [collective anomalies](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-2/#collective-anomalies) or [novelties](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-2/#novelties) (expanded to multi-input scenario). For example, you may set it up for anomaly detection of CPU usage in different modes (`idle`, `user`, `system`, etc.) and use its cross-dependencies to detect **unseen (in fit data)** behavior.

-**Examples:** [Temporal Envelope](#temporal-envelope), [Isolation Forest](#isolation-forest-multivariate)
+**Recommended:** [Temporal Envelope](#temporal-envelope). Existing [Isolation Forest](#isolation-forest-multivariate) configurations can migrate to its multivariate form.

 ![Multivariate model lifecycle](model-lifecycle-multivariate.svg)

-The following configuration applies both models to the same aligned input series. Start with Temporal Envelope when temporal profiles and online adaptation matter; use Isolation Forest as an offline alternative when feature-space outliers are the primary concern.
+The following configuration applies a multivariate Temporal Envelope model to the same aligned input series:

 ```yaml
 models:
@@ -582,16 +580,6 @@ models:
    seasonalities: [hod_smooth, dow_smooth]
    provide_series: [anomaly_score]

-  service_dependency_isolation_forest:
-    class: isolation_forest_multivariate
-    queries: [request_rate, error_rate, latency]
-    groupby: [cluster]
-    contamination: 0.01
-    seasonal_features: [hod, dow]
-    args:
-      n_estimators: 100
-      random_state: 42
-    provide_series: [anomaly_score]
 ```


@@ -634,6 +622,9 @@ Each of the ([built-in](#built-in-models) or [custom](#custom-model-guide)) onli

 Every other model that isn't [online](#online-models). Offline models are completely re-trained during `fit` call and aren't updated during consecutive `infer` calls.

+> [!NOTE]
+> Built-in offline model classes are planned for deprecation in a future release in favor of online counterparts. For complex temporal data, prefer [Temporal Envelope](#temporal-envelope), which supports incremental adaptation, forecasting, and both univariate and multivariate operation.
+

 ## Built-in Models 

@@ -649,14 +640,14 @@ Built-in models support 2 groups of arguments:
 **Models**:
 - [AutoTuned](#autotuned) - designed to take the cognitive load off the user, allowing any of built-in models below to be re-tuned for best hyperparameters on data seen during each `fit` phase of the algorithm. Tradeoff is between increased computational time and optimized results / simpler maintenance.
 - [Temporal Envelope](#temporal-envelope) - the preferred **online model for complex operational data** with trends, changepoints, multiple calendar patterns, holidays, capable of [forecasting](https://docs.victoriametrics.com/anomaly-detection/faq/#forecasting). Its multivariate form also learns cross-series relationships.
- [Prophet](#prophet) - an offline forecasting alternative when Prophet-specific decomposition outputs are required. Favor `Temporal Envelope` for online adaptation and multivariate support.
 - [Online Z-score](#online-z-score) - useful for initial testing and for simpler data ([de-trended](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#trend) data without strict [seasonality](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#seasonality) and with anomalies of similar magnitude as your "normal" data)
 - [MAD](#online-mad) - similarly to [Z-score](#online-z-score), is effective for **identifying outliers in relatively consistent data**. Useful for detecting sudden, stark deviations from the median, being less prone to outlier's magnitude than z-score.
 - [Rolling Quantile](#rolling-quantile) - best for **data with evolving patterns**, as it adapts to changes over a rolling window.
 - [Online Seasonal Quantile](#online-seasonal-quantile) - best used on **[de-trended](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#trend) data with strong (possibly multiple) [seasonalities](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#seasonality)**. Can act as a (slightly less powerful) [online](#online-models) replacement to [`ProphetModel`](#prophet).
 - [Seasonal Trend Decomposition](#seasonal-trend-decomposition) - similarly to Holt-Winters, is best for **data with pronounced [seasonal](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#seasonality) and [trend](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#trend) components**
- [Isolation forest (Multivariate)](#isolation-forest-multivariate) - an offline alternative for **metrics data interaction** (several queries/metrics -> single anomaly score) and high-dimensional feature-space outliers. Prefer multivariate Temporal Envelope when temporal profiles and *online* adaptation matter.
- [Holt-Winters](#holt-winters) - well-suited for **data with moderate complexity**, exhibiting distinct [trends](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#trend) and/or [single seasonal pattern](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#seasonality).
+- [Prophet](#prophet) - an offline model retained for existing deployments. Migrate forecasting and seasonal anomaly-detection configurations to [Temporal Envelope](#temporal-envelope), unless Prophet-specific decomposition output must be preserved.
+- [Isolation forest (Multivariate)](#isolation-forest-multivariate) - an offline model retained for existing univariate and multivariate deployments. Migrate to the corresponding [Temporal Envelope](#temporal-envelope) form for online adaptation and temporal or cross-series context.
+- [Holt-Winters](#holt-winters) - an offline model retained for existing trend and single-seasonality configurations. Migrate these configurations to [Temporal Envelope](#temporal-envelope).
 - [Custom model](#custom-model-guide) - benefit from your own models and expertise to better support your **unique use case**.


@@ -785,6 +776,8 @@ The requested anomaly percentage is treated as an alert-volume constraint rather

 {{% available_from "v1.30.0" anomaly %}} Temporal Envelope is the preferred online model for complex operational and business metrics. It learns an evolving expected range from robust trend, calendar and holiday patterns, persistent level shifts, uncertainty, and optional future forecasts. The model adapts during inference while limiting the lasting influence of short-lived spikes.

+{{% available_from "v1.30.1" anomaly %}} When the fit window ends in a recently established level, the model initializes its adaptive state from that recent regime while preserving supported calendar structure. This improves the first periodic predictions after a level shift and reduces false seasonal oscillation without requiring additional configuration.
+
 > `TemporalEnvelopeModel` is [univariate](#univariate-models) and [online](#online-models). `TemporalEnvelopeMultivariateModel` also learns normal cross-series relationships as a [multivariate](#multivariate-models) model.

 Use it for:
@@ -793,7 +786,7 @@ Use it for:
 - deployments, traffic migrations, and capacity changes that create persistent shifts, including short-horizon forecasts through `forecast_at`;
 - aligned related metrics where each channel keeps its own temporal pattern while their joint behavior contributes to one anomaly score.

-For simple profiles without strong trend or seasonality, prefer [Online MAD](#online-mad) or [Online Z-score](#online-z-score). [Prophet](#prophet) and [Isolation Forest](#isolation-forest-multivariate) remain offline alternatives when their distinct capabilities are required or validation favors them.
+For simple profiles without strong trend or seasonality, prefer [Online MAD](#online-mad) or [Online Z-score](#online-z-score). Existing [Prophet](#prophet) and [Isolation Forest](#isolation-forest-multivariate) configurations can be migrated to the corresponding univariate or multivariate Temporal Envelope form.

 <div class="model-details">

@@ -873,11 +866,224 @@ For independent per-series detection, use `temporal_envelope`. Use `temporal_env

 </div>

+### Online MAD
+
+> `OnlineMADModel` is a [univariate](#univariate-models), [online](#online-models) model.
+
+The MAD model is a robust method for anomaly detection that is *less sensitive* to outliers in data compared to standard deviation-based models. It considers a point as an anomaly if the absolute deviation from the median is significantly large. This is the online approximate version, based on [t-digests](https://www.sciencedirect.com/science/article/pii/S2665963820300403) for online quantile estimation{{% available_from "v1.15.0" anomaly %}}.
+
+<div class="model-details">
+
+{{% collapse name="Model-specific arguments" %}}
+
+- `class` (string) - model class name `"model.online.OnlineMADModel"` (or `mad_online` with class alias support{{% available_from "v1.13.0" anomaly %}})
+- `threshold` (float, optional) - The threshold multiplier for the MAD to determine anomalies. Defaults to `2.5`. Higher values will identify fewer points as anomalies.
+- `min_n_samples_seen` (int, optional) - the minimum number of samples to be seen (`n_samples_seen_` property) before computing the anomaly score. Otherwise, the **anomaly score will be 0**, as there is not enough data to trust the model's predictions. Defaults to 16.
+- `history_strength` (float, optional) - {{% available_from "v1.30.0" anomaly %}} strength of the initial history learned by `fit`. Values above `1` preserve fitted quantiles initially but reduce the leverage of subsequent updates. Defaults to `1`.
+- `compression` (int, optional) - the compression parameter for  underlying [t-digest](https://www.sciencedirect.com/science/article/pii/S2665963820300403). Higher values mean higher accuracy but higher memory usage. By default 100.
+
+{{% /collapse %}}
+
+{{% collapse name="Configuration example" %}}
+
+
+```yaml
+models:
+  your_desired_alias_for_a_model:
+    class: "mad_online"  # or 'model.online.OnlineMADModel'
+    threshold: 2.5
+    min_n_samples_seen: 128  # i.e. calculate it as full seasonality / data freq
+    history_strength: 2  # retain fitted history as a stronger prior
+    compression: 100  # higher values mean higher accuracy but higher memory usage
+    provide_series: ['anomaly_score', 'yhat']  # common arg example
+    # Common arguments for built-in model, if not set, default to
+    # See https://docs.victoriametrics.com/anomaly-detection/components/models/#common-args
+    #
+    # provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper']
+    # schedulers: [all scheduler aliases defined in `scheduler` section]
+    # queries: [all query aliases defined in `reader.queries` section]
+    # detection_direction: 'both'  # meaning both drops and spikes will be captured
+    # min_dev_from_expected: [0.0, 0.0]  # meaning, no minimal threshold is applied to prevent smaller anomalies
+    # scale: [1.0, 1.0]  # if needed, prediction intervals' width can be increased (>1) or narrowed (<1)
+    # clip_predictions: False  # if data_range for respective `queries` is set in reader, `yhat.*` columns will be clipped
+    # anomaly_score_outside_data_range: 1.01  # auto anomaly score (1.01) if `y` (real value) is outside of data_range, if set
+```
+
+{{% /collapse %}}
+
+</div>
+
+Resulting metrics of the model are described [here](#vmanomaly-output).
+
+
+### Online Seasonal Quantile
+
+> `OnlineQuantileModel` is a [univariate](#univariate-models), [online](#online-models) model.
+
+Online (seasonal) quantile utilizes a set of approximate distributions, based on [t-digests](https://www.sciencedirect.com/science/article/pii/S2665963820300403) for online quantile estimation {{% available_from "v1.15.0" anomaly %}}.
+
+Best used on **[de-trended](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#trend) data with strong (potentially multiple) [seasonalities](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#seasonality)**. Can act as a (slightly less flexible) replacement to [`ProphetModel`](#prophet).
+
+It uses the `quantiles` triplet to calculate `yhat_lower`, `yhat`, and `yhat_upper` [output](#vmanomaly-output), respectively, for each of the `min_subseason` sub-intervals contained in `seasonal_interval`. For example, with '4d' + '2h' seasonality patterns (multiple), it will hold and update 24*4 / 2 = 48 consecutive estimates (each 2 hours long).
+
+<div class="model-details">
+
+{{% collapse name="Model-specific arguments" %}}
+
+- `class` (string) - model class name `"model.online.OnlineQuantileModel"` (or `quantile_online` with class alias support{{% available_from "v1.13.0" anomaly %}})
+- `quantiles` (list[float], optional) - The quantiles to estimate. `yhat_lower`, `yhat`, `yhat_upper` are the quantile order. By default (0.01, 0.5, 0.99).
+- `iqr_threshold` (float, optional) - {{% available_from "v1.25.0" anomaly %}} The [interquartile range (IQR)](https://en.wikipedia.org/wiki/Interquartile_range) multiplier to increase the width of the prediction intervals. Defaults to 0 (no adjustment) for backward compatibility. If set > 0, the model will add IQR * `iqr_threshold` to `yhat_lower` and  `yhat_upper` (respecting `min_subseason` seasonal buckets). This is useful for data with high variance or outliers, as it helps to avoid false positives in anomaly detection. Best used with **robust** `quantiles` set to (0.25, 0.5, 0.75) or similar.
+- `seasonal_interval` (string, optional) - the interval for the seasonal adjustment. If not set, the model will equal to a simple online quantile model. By default not set.
+- `min_subseason` (str, optional) - the minimum interval to estimate quantiles for. By default not set. Note that the minimum interval should be a multiple of the seasonal interval, i.e. if seasonal_interval='2h', then min_subseason='15m' is valid, but '37m' is not.
+- `use_transform` (bool, optional) - whether to internally apply a `log1p(abs(x)) * sign(x)` transformation to the data to stabilize internal quantile estimation. Does not affect the scale of produced output (i.e. `yhat`) By default False.
+- `global_smoothing` (float, optional) - the smoothing parameter for the global quantiles. i.e. the output is a weighted average of the global and seasonal quantiles (if `seasonal_interval` and `min_subseason` args are set). Should be from `[0, 1]` interval, where 0 means no smoothing and 1 means using only global quantile values.
+- `scale` (float, optional) - Is used to adjust the margins between `yhat` and [`yhat_lower`, `yhat_upper`]. New margin = `|yhat_* - yhat_lower| * scale`. Defaults to 1 (no scaling is applied). See `scale`[common arg](https://docs.victoriametrics.com/anomaly-detection/components/models/#scale) section for detailed instructions and 2-sided option.
+- `season_starts_from` (str, optional) - the start date for the seasonal adjustment, as a reference point to start counting the intervals. By default '1970-01-01'.
+- `min_n_samples_seen` (int, optional) - the minimum number of samples to be seen (`n_samples_seen_` property) before computing the anomaly score. Otherwise, the **anomaly score will be 0**, as there is not enough data to trust the model's predictions. Defaults to 16.
+- `history_strength` (float, optional) - {{% available_from "v1.30.0" anomaly %}} strength of the initial history learned by `fit`. Values above `1` preserve fitted quantiles initially but reduce the leverage of subsequent updates. Defaults to `1`.
+- `compression` (int, optional) - the compression parameter for the underlying [t-digests](https://www.sciencedirect.com/science/article/pii/S2665963820300403). Higher values mean higher accuracy but higher memory usage. By default 100.
+
+{{% /collapse %}}
+
+{{% collapse name="Configuration example" %}}
+
+Suppose we have a data with strong intra-day (hourly) and intra-week (daily) seasonality, data granularity is '5m' with up to 5% expected outliers present in data. Then you can apply similar config:
+
+```yaml
+models:
+  your_desired_alias_for_a_model:
+    class: "quantile_online"  # or 'model.online.OnlineQuantileModel'
+    quantiles: [0.25, 0.5, 0.75]  # lowered to exclude anomalous edges, can be compensated by `scale` param > 1 and `iqr_threshold` > 0
+    iqr_threshold: 2.5  # to increase prediction intervals' width to avoid false positives while still keeping the model robust
+    seasonal_interval: '7d'  # longest seasonality (week, day) = week, starting from `season_starts_from`
+    min_subseason: '1h'  # smallest seasonality (week, day, hour) = hour, will have its own quantile estimates
+    min_n_samples_seen: 288 # 1440 / 5 - at least 1 full day, ideal = 1440 / 5 * 7 - one full week (seasonal_interval)
+    history_strength: 2  # retain fitted history as a stronger prior
+    scale: 1.1  # to compensate lowered quantile boundaries with wider intervals
+    season_starts_from: '2024-01-01'  # interval calculation starting point, especially for uncommon seasonalities like '36h' or '12d'
+    compression: 100  # higher values mean higher accuracy but higher memory usage
+    provide_series: ['anomaly_score', 'yhat']  # common arg example
+    # Common arguments for built-in model, if not set, default to
+    # See https://docs.victoriametrics.com/anomaly-detection/components/models/#common-args
+    #
+    # provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper']
+    # schedulers: [all scheduler aliases defined in `scheduler` section]
+    # queries: [all query aliases defined in `reader.queries` section]
+    # detection_direction: 'both'  # meaning both drops and spikes will be captured
+    # min_dev_from_expected: [0.0, 0.0]  # meaning, no minimal threshold is applied to prevent smaller anomalies
+    # scale: [1.0, 1.0]  # if needed, prediction intervals' width can be increased (>1) or narrowed (<1)
+    # clip_predictions: False  # if data_range for respective `queries` is set in reader, `yhat.*` columns will be clipped
+    # anomaly_score_outside_data_range: 1.01  # auto anomaly score (1.01) if `y` (real value) is outside of data_range, if set
+```
+
+{{% /collapse %}}
+
+</div>
+
+Resulting metrics of the model are described [here](#vmanomaly-output).
+
+
+### Online Z-score
+
+> `OnlineZscoreModel` is a [univariate](#univariate-models), [online](#online-models) model.
+
+Online version of existing [Z-score](#z-score) implementation with the same exact behavior and implications {{% available_from "v1.15.0" anomaly %}}.
+
+<div class="model-details">
+
+{{% collapse name="Model-specific arguments" %}}
+
+- `class` (string) - model class name `"model.online.OnlineZscoreModel"` (or `zscore_online`with class alias support{{% available_from "v1.13.0" anomaly %}})
+- `z_threshold` (float, optional) - [standard score](https://en.wikipedia.org/wiki/Standard_score) for calculation boundaries and anomaly score. Defaults to `2.5`.
+- `min_n_samples_seen` (int, optional) - the minimum number of samples to be seen (`n_samples_seen_` property) before computing the anomaly score. Otherwise, the **anomaly score will be 0**, as there is not enough data to trust the model's predictions. Defaults to 16.
+- `history_strength` (float, optional) - {{% available_from "v1.30.0" anomaly %}} strength of the initial history learned by `fit`. Values above `1` keep fitted mean and variance unchanged initially but reduce the leverage of subsequent updates. Defaults to `1`.
+
+{{% /collapse %}}
+
+{{% collapse name="Configuration example" %}}
+
+```yaml
+models:
+  your_desired_alias_for_a_model:
+    class: "zscore_online"  # or 'model.online.OnlineZscoreModel'
+    z_threshold: 3.5
+    min_n_samples_seen: 128  # i.e. calculate it as full seasonality / data freq
+    history_strength: 2  # retain fitted history as a stronger prior
+    provide_series: ['anomaly_score', 'yhat']  # common arg example
+    # Common arguments for built-in model, if not set, default to
+    # See https://docs.victoriametrics.com/anomaly-detection/components/models/#common-args
+    #
+    # provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper']
+    # schedulers: [all scheduler aliases defined in `scheduler` section]
+    # queries: [all query aliases defined in `reader.queries` section]
+    # detection_direction: 'both'  # meaning both drops and spikes will be captured
+    # min_dev_from_expected: [0.0, 0.0]  # meaning, no minimal threshold is applied to prevent smaller anomalies
+    # scale: [1.0, 1.0]  # if needed, prediction intervals' width can be increased (>1) or narrowed (<1)
+    # clip_predictions: False  # if data_range for respective `queries` is set in reader, `yhat.*` columns will be clipped
+    # anomaly_score_outside_data_range: 1.01  # auto anomaly score (1.01) if `y` (real value) is outside of data_range, if set
+```
+
+{{% /collapse %}}
+
+</div>
+
+Resulting metrics of the model are described [here](#vmanomaly-output).
+
+
+### [Rolling Quantile](https://en.wikipedia.org/wiki/Quantile)
+
+> `RollingQuantileModel` **is** {{% available_from "v1.28.0" anomaly %}} a [univariate](#univariate-models), [online](#online-models) model. It **was** {{% deprecated_from "v1.28.0" anomaly %}} a [univariate](#univariate-models), [rolling](#rolling-models), [offline](#offline-models) model.
+
+This model is best used on **data with short evolving patterns** (i.e. 10-100 datapoints of particular frequency), as it adapts to changes over a rolling window.
+
+<div class="model-details">
+
+{{% collapse name="Model-specific arguments" %}}
+
+- `class` (string) - model class name `"model.rolling_quantile.RollingQuantileModel"` (or `rolling_quantile` with class alias support {{% available_from "v1.13.0" anomaly %}})
+- `quantile` (float) - quantile value, from 0.5 to 1.0. This constraint is implied by 2-sided confidence interval.
+- `window_steps` (integer) - size of the moving window. (see 'sampling_period')
+- `iqr_threshold` (float, optional) - {{% available_from "v1.25.0" anomaly %}} The [interquartile range (IQR)](https://en.wikipedia.org/wiki/Interquartile_range) multiplier to increase the width of the prediction intervals. Defaults to 0 (no adjustment) for backward compatibility. If set > 0, the model will add half IQR * `iqr_threshold` to `yhat_lower` and  `yhat_upper`. This is useful for data with high variance or outliers, as it helps to avoid false positives in anomaly detection.
+
+{{% /collapse %}}
+
+{{% collapse name="Configuration example" %}}
+
+```yaml
+models:
+  your_desired_alias_for_a_model:
+    class: "rolling_quantile"
+    quantile: 0.9
+    window_steps: 96
+    iqr_threshold: 1
+    # Common arguments for built-in model, if not set, default to
+    # See https://docs.victoriametrics.com/anomaly-detection/components/models/#common-args
+    #
+    # provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper']
+    # schedulers: [all scheduler aliases defined in `scheduler` section]
+    # queries: [all query aliases defined in `reader.queries` section]
+    # detection_direction: 'both'  # meaning both drops and spikes will be captured
+    # min_dev_from_expected: [0.0, 0.0]  # meaning, no minimal threshold is applied to prevent smaller anomalies
+    # scale: [1.0, 1.0]  # if needed, prediction intervals' width can be increased (>1) or narrowed (<1)
+    # clip_predictions: False  # if data_range for respective `queries` is set in reader, `yhat.*` columns will be clipped
+    # anomaly_score_outside_data_range: 1.01  # auto anomaly score (1.01) if `y` (real value) is outside of data_range, if set
+```
+
+{{% /collapse %}}
+
+</div>
+
+Resulting metrics of the model are described [here](#vmanomaly-output).
+
+
 ### [Prophet](https://facebook.github.io/prophet/)
 `vmanomaly` uses the Facebook Prophet implementation for time series forecasting, with detailed usage provided in the [Prophet library documentation](https://facebook.github.io/prophet/docs/quick_start#python-api). All original Prophet parameters are supported and can be directly passed to the model via `args` argument.

 > `ProphetModel` is a [univariate](#univariate-models), [offline](#offline-models) model.

+> [!NOTE]
+> Prophet is planned for deprecation in a future release. For new forecasting and anomaly-detection deployments, prefer the online [Temporal Envelope](#temporal-envelope) model unless Prophet-specific decomposition output is required.
+
 > {{% available_from "v1.25.3" anomaly %}} Producing forecasts for future timestamps is now supported. To enable this, set the `forecast_at` argument to a list of relative future offsets (e.g., `['1h', '1d']`). The model will then generate forecasts for these future timestamps, which can be useful for planning and resource allocation. Output series are affected by [provide_series](#provide-series) argument, which need to include at least `yhat` for point-wise forecasts (and `yhat_lower` or/and `yhat_upper` for respective confidence intervals). See the example below for more details.

 <div class="model-details">
@@ -993,271 +1199,15 @@ Depending on chosen `seasonality` parameter FB Prophet can return additional met

 Resulting metrics of the model are described [here](#vmanomaly-output)

-### Online Z-score
-
-> `OnlineZscoreModel` is a [univariate](#univariate-models), [online](#online-models) model.
-
-Online version of existing [Z-score](#z-score) implementation with the same exact behavior and implications {{% available_from "v1.15.0" anomaly %}}.
-
-<div class="model-details">
-
-{{% collapse name="Model-specific arguments" %}}
-
- `class` (string) - model class name `"model.online.OnlineZscoreModel"` (or `zscore_online`with class alias support{{% available_from "v1.13.0" anomaly %}})
- `z_threshold` (float, optional) - [standard score](https://en.wikipedia.org/wiki/Standard_score) for calculation boundaries and anomaly score. Defaults to `2.5`.
- `min_n_samples_seen` (int, optional) - the minimum number of samples to be seen (`n_samples_seen_` property) before computing the anomaly score. Otherwise, the **anomaly score will be 0**, as there is not enough data to trust the model's predictions. Defaults to 16.
- `history_strength` (float, optional) - {{% available_from "v1.30.0" anomaly %}} strength of the initial history learned by `fit`. Values above `1` keep fitted mean and variance unchanged initially but reduce the leverage of subsequent updates. Defaults to `1`.
-
-{{% /collapse %}}
-
-{{% collapse name="Configuration example" %}}
-
-```yaml
-models:
-  your_desired_alias_for_a_model:
-    class: "zscore_online"  # or 'model.online.OnlineZscoreModel'
-    z_threshold: 3.5
-    min_n_samples_seen: 128  # i.e. calculate it as full seasonality / data freq
-    history_strength: 2  # retain fitted history as a stronger prior
-    provide_series: ['anomaly_score', 'yhat']  # common arg example
-    # Common arguments for built-in model, if not set, default to
-    # See https://docs.victoriametrics.com/anomaly-detection/components/models/#common-args
-    #
-    # provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper']
-    # schedulers: [all scheduler aliases defined in `scheduler` section]
-    # queries: [all query aliases defined in `reader.queries` section]
-    # detection_direction: 'both'  # meaning both drops and spikes will be captured
-    # min_dev_from_expected: [0.0, 0.0]  # meaning, no minimal threshold is applied to prevent smaller anomalies
-    # scale: [1.0, 1.0]  # if needed, prediction intervals' width can be increased (>1) or narrowed (<1)
-    # clip_predictions: False  # if data_range for respective `queries` is set in reader, `yhat.*` columns will be clipped
-    # anomaly_score_outside_data_range: 1.01  # auto anomaly score (1.01) if `y` (real value) is outside of data_range, if set
-```
-
-{{% /collapse %}}
-
-</div>
-
-Resulting metrics of the model are described [here](#vmanomaly-output).
-
-
-### Online MAD
-
-> `OnlineMADModel` is a [univariate](#univariate-models), [online](#online-models) model.
-
-The MAD model is a robust method for anomaly detection that is *less sensitive* to outliers in data compared to standard deviation-based models. It considers a point as an anomaly if the absolute deviation from the median is significantly large. This is the online approximate version, based on [t-digests](https://www.sciencedirect.com/science/article/pii/S2665963820300403) for online quantile estimation{{% available_from "v1.15.0" anomaly %}}.
-
-<div class="model-details">
-
-{{% collapse name="Model-specific arguments" %}}
-
- `class` (string) - model class name `"model.online.OnlineMADModel"` (or `mad_online` with class alias support{{% available_from "v1.13.0" anomaly %}})
- `threshold` (float, optional) - The threshold multiplier for the MAD to determine anomalies. Defaults to `2.5`. Higher values will identify fewer points as anomalies.
- `min_n_samples_seen` (int, optional) - the minimum number of samples to be seen (`n_samples_seen_` property) before computing the anomaly score. Otherwise, the **anomaly score will be 0**, as there is not enough data to trust the model's predictions. Defaults to 16.
- `history_strength` (float, optional) - {{% available_from "v1.30.0" anomaly %}} strength of the initial history learned by `fit`. Values above `1` preserve fitted quantiles initially but reduce the leverage of subsequent updates. Defaults to `1`.
- `compression` (int, optional) - the compression parameter for  underlying [t-digest](https://www.sciencedirect.com/science/article/pii/S2665963820300403). Higher values mean higher accuracy but higher memory usage. By default 100.
-
-{{% /collapse %}}
-
-{{% collapse name="Configuration example" %}}
-
-
-```yaml
-models:
-  your_desired_alias_for_a_model:
-    class: "mad_online"  # or 'model.online.OnlineMADModel'
-    threshold: 2.5
-    min_n_samples_seen: 128  # i.e. calculate it as full seasonality / data freq
-    history_strength: 2  # retain fitted history as a stronger prior
-    compression: 100  # higher values mean higher accuracy but higher memory usage
-    provide_series: ['anomaly_score', 'yhat']  # common arg example
-    # Common arguments for built-in model, if not set, default to
-    # See https://docs.victoriametrics.com/anomaly-detection/components/models/#common-args
-    #
-    # provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper']
-    # schedulers: [all scheduler aliases defined in `scheduler` section]
-    # queries: [all query aliases defined in `reader.queries` section]
-    # detection_direction: 'both'  # meaning both drops and spikes will be captured
-    # min_dev_from_expected: [0.0, 0.0]  # meaning, no minimal threshold is applied to prevent smaller anomalies
-    # scale: [1.0, 1.0]  # if needed, prediction intervals' width can be increased (>1) or narrowed (<1)
-    # clip_predictions: False  # if data_range for respective `queries` is set in reader, `yhat.*` columns will be clipped
-    # anomaly_score_outside_data_range: 1.01  # auto anomaly score (1.01) if `y` (real value) is outside of data_range, if set
-```
-
-{{% /collapse %}}
-
-</div>
-
-Resulting metrics of the model are described [here](#vmanomaly-output).
-
-
-### [Rolling Quantile](https://en.wikipedia.org/wiki/Quantile)
-
-> `RollingQuantileModel` **is** {{% available_from "v1.28.0" anomaly %}} a [univariate](#univariate-models), [online](#online-models) model. It **was** {{% deprecated_from "v1.28.0" anomaly %}} a [univariate](#univariate-models), [rolling](#rolling-models), [offline](#offline-models) model.
-
-This model is best used on **data with short evolving patterns** (i.e. 10-100 datapoints of particular frequency), as it adapts to changes over a rolling window.
-
-<div class="model-details">
-
-{{% collapse name="Model-specific arguments" %}}
-
- `class` (string) - model class name `"model.rolling_quantile.RollingQuantileModel"` (or `rolling_quantile` with class alias support {{% available_from "v1.13.0" anomaly %}})
- `quantile` (float) - quantile value, from 0.5 to 1.0. This constraint is implied by 2-sided confidence interval.
- `window_steps` (integer) - size of the moving window. (see 'sampling_period')
- `iqr_threshold` (float, optional) - {{% available_from "v1.25.0" anomaly %}} The [interquartile range (IQR)](https://en.wikipedia.org/wiki/Interquartile_range) multiplier to increase the width of the prediction intervals. Defaults to 0 (no adjustment) for backward compatibility. If set > 0, the model will add half IQR * `iqr_threshold` to `yhat_lower` and  `yhat_upper`. This is useful for data with high variance or outliers, as it helps to avoid false positives in anomaly detection.
-
-{{% /collapse %}}
-
-{{% collapse name="Configuration example" %}}
-
-```yaml
-models:
-  your_desired_alias_for_a_model:
-    class: "rolling_quantile"
-    quantile: 0.9
-    window_steps: 96
-    iqr_threshold: 1
-    # Common arguments for built-in model, if not set, default to
-    # See https://docs.victoriametrics.com/anomaly-detection/components/models/#common-args
-    #
-    # provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper']
-    # schedulers: [all scheduler aliases defined in `scheduler` section]
-    # queries: [all query aliases defined in `reader.queries` section]
-    # detection_direction: 'both'  # meaning both drops and spikes will be captured
-    # min_dev_from_expected: [0.0, 0.0]  # meaning, no minimal threshold is applied to prevent smaller anomalies
-    # scale: [1.0, 1.0]  # if needed, prediction intervals' width can be increased (>1) or narrowed (<1)
-    # clip_predictions: False  # if data_range for respective `queries` is set in reader, `yhat.*` columns will be clipped
-    # anomaly_score_outside_data_range: 1.01  # auto anomaly score (1.01) if `y` (real value) is outside of data_range, if set
-```
-
-{{% /collapse %}}
-
-</div>
-
-Resulting metrics of the model are described [here](#vmanomaly-output).
-
-
-### Online Seasonal Quantile
-
-> `OnlineQuantileModel` is a [univariate](#univariate-models), [online](#online-models) model.
-
-Online (seasonal) quantile utilizes a set of approximate distributions, based on [t-digests](https://www.sciencedirect.com/science/article/pii/S2665963820300403) for online quantile estimation {{% available_from "v1.15.0" anomaly %}}.
-
-Best used on **[de-trended](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#trend) data with strong (potentially multiple) [seasonalities](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#seasonality)**. Can act as a (slightly less flexible) replacement to [`ProphetModel`](#prophet).
-
-It uses the `quantiles` triplet to calculate `yhat_lower`, `yhat`, and `yhat_upper` [output](#vmanomaly-output), respectively, for each of the `min_subseasons` sub-intervals contained in `seasonal_interval`. For example, with '4d' + '2h' seasonality patterns (multiple), it will hold and update 24*4 / 2 = 48 consecutive estimates (each 2 hours long).
-
-<div class="model-details">
-
-{{% collapse name="Model-specific arguments" %}}
-
- `class` (string) - model class name `"model.online.OnlineQuantileModel"` (or `quantile_online` with class alias support{{% available_from "v1.13.0" anomaly %}})
- `quantiles` (list[float], optional) - The quantiles to estimate. `yhat_lower`, `yhat`, `yhat_upper` are the quantile order. By default (0.01, 0.5, 0.99).
- `iqr_threshold` (float, optional) - {{% available_from "v1.25.0" anomaly %}} The [interquartile range (IQR)](https://en.wikipedia.org/wiki/Interquartile_range) multiplier to increase the width of the prediction intervals. Defaults to 0 (no adjustment) for backward compatibility. If set > 0, the model will add IQR * `iqr_threshold` to `yhat_lower` and  `yhat_upper` (respecting `min_subseason` seasonal buckets). This is useful for data with high variance or outliers, as it helps to avoid false positives in anomaly detection. Best used with **robust** `quantiles` set to (0.25, 0.5, 0.75) or similar.
- `seasonal_interval` (string, optional) - the interval for the seasonal adjustment. If not set, the model will equal to a simple online quantile model. By default not set.
- `min_subseason` (str, optional) - the minimum interval to estimate quantiles for. By default not set. Note that the minimum interval should be a multiple of the seasonal interval, i.e. if seasonal_interval='2h', then min_subseason='15m' is valid, but '37m' is not.
- `use_transform` (bool, optional) - whether to internally apply a `log1p(abs(x)) * sign(x)` transformation to the data to stabilize internal quantile estimation. Does not affect the scale of produced output (i.e. `yhat`) By default False.
- `global_smoothing` (float, optional) - the smoothing parameter for the global quantiles. i.e. the output is a weighted average of the global and seasonal quantiles (if `seasonal_interval` and `min_subseason` args are set). Should be from `[0, 1]` interval, where 0 means no smoothing and 1 means using only global quantile values.
- `scale` (float, optional) - Is used to adjust the margins between `yhat` and [`yhat_lower`, `yhat_upper`]. New margin = `|yhat_* - yhat_lower| * scale`. Defaults to 1 (no scaling is applied). See `scale`[common arg](https://docs.victoriametrics.com/anomaly-detection/components/models/#scale) section for detailed instructions and 2-sided option.
- `season_starts_from` (str, optional) - the start date for the seasonal adjustment, as a reference point to start counting the intervals. By default '1970-01-01'.
- `min_n_samples_seen` (int, optional) - the minimum number of samples to be seen (`n_samples_seen_` property) before computing the anomaly score. Otherwise, the **anomaly score will be 0**, as there is not enough data to trust the model's predictions. Defaults to 16.
- `history_strength` (float, optional) - {{% available_from "v1.30.0" anomaly %}} strength of the initial history learned by `fit`. Values above `1` preserve fitted quantiles initially but reduce the leverage of subsequent updates. Defaults to `1`.
- `compression` (int, optional) - the compression parameter for the underlying [t-digests](https://www.sciencedirect.com/science/article/pii/S2665963820300403). Higher values mean higher accuracy but higher memory usage. By default 100.
-
-{{% /collapse %}}
-
-{{% collapse name="Configuration example" %}}
-
-Suppose we have a data with strong intra-day (hourly) and intra-week (daily) seasonality, data granularity is '5m' with up to 5% expected outliers present in data. Then you can apply similar config:
-
-```yaml
-models:
-  your_desired_alias_for_a_model:
-    class: "quantile_online"  # or 'model.online.OnlineQuantileModel'
-    quantiles: [0.25, 0.5, 0.75]  # lowered to exclude anomalous edges, can be compensated by `scale` param > 1 and `iqr_threshold` > 0
-    iqr_threshold: 2.5  # to increase prediction intervals' width to avoid false positives while still keeping the model robust
-    seasonal_interval: '7d'  # longest seasonality (week, day) = week, starting from `season_starts_from`
-    min_subseason: '1h'  # smallest seasonality (week, day, hour) = hour, will have its own quantile estimates
-    min_n_samples_seen: 288 # 1440 / 5 - at least 1 full day, ideal = 1440 / 5 * 7 - one full week (seasonal_interval)
-    history_strength: 2  # retain fitted history as a stronger prior
-    scale: 1.1  # to compensate lowered quantile boundaries with wider intervals
-    season_starts_from: '2024-01-01'  # interval calculation starting point, especially for uncommon seasonalities like '36h' or '12d'
-    compression: 100  # higher values mean higher accuracy but higher memory usage
-    provide_series: ['anomaly_score', 'yhat']  # common arg example
-    # Common arguments for built-in model, if not set, default to
-    # See https://docs.victoriametrics.com/anomaly-detection/components/models/#common-args
-    #
-    # provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper']
-    # schedulers: [all scheduler aliases defined in `scheduler` section]
-    # queries: [all query aliases defined in `reader.queries` section]
-    # detection_direction: 'both'  # meaning both drops and spikes will be captured
-    # min_dev_from_expected: [0.0, 0.0]  # meaning, no minimal threshold is applied to prevent smaller anomalies
-    # scale: [1.0, 1.0]  # if needed, prediction intervals' width can be increased (>1) or narrowed (<1)
-    # clip_predictions: False  # if data_range for respective `queries` is set in reader, `yhat.*` columns will be clipped
-    # anomaly_score_outside_data_range: 1.01  # auto anomaly score (1.01) if `y` (real value) is outside of data_range, if set
-```
-
-{{% /collapse %}}
-
-</div>
-
-Resulting metrics of the model are described [here](#vmanomaly-output).
-
-
-### [Seasonal Trend Decomposition](https://en.wikipedia.org/wiki/Seasonal_adjustment)
-
-> `StdModel` **is** {{% available_from "v1.28.0" anomaly %}} a [univariate](#univariate-models), [online](#online-models) model. It **was** {{% deprecated_from "v1.28.0" anomaly %}} a [univariate](#univariate-models), [rolling](#rolling-models), [offline](#offline-models) model.
-
-Here we use Seasonal Decompose implementation from `statsmodels` [library](https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.seasonal_decompose). Parameters from this library can be passed to the model. Some parameters are specifically predefined in `vmanomaly` and can't be changed by user (`model`='additive', `two_sided`=False).
-
-<div class="model-details">
-
-{{% collapse name="Model-specific arguments" %}}
-
- `class` (string) - model class name `"model.std.StdModel"` (or `std` with class alias support{{% available_from "v1.13.0" anomaly %}})
- `period` (integer) -  Number of datapoints in one season.
- `z_threshold` (float, optional) - [standard score](https://en.wikipedia.org/wiki/Standard_score) for calculating boundaries to define anomaly score. Defaults to `2.5`.
-
-{{% /collapse %}}
-
-{{% collapse name="Configuration example" %}}
-
-
-```yaml
-models:
-  your_desired_alias_for_a_model:
-    class: "std"  # or 'model.std.StdModel' starting from v1.13.0
-    period: 2
-    # Common arguments for built-in model, if not set, default to
-    # See https://docs.victoriametrics.com/anomaly-detection/components/models/#common-args
-    #
-    # provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper']
-    # schedulers: [all scheduler aliases defined in `scheduler` section]
-    # queries: [all query aliases defined in `reader.queries` section]
-    # detection_direction: 'both'  # meaning both drops and spikes will be captured
-    # min_dev_from_expected: [0.0, 0.0]  # meaning, no minimal threshold is applied to prevent smaller anomalies
-    # scale: [1.0, 1.0]  # if needed, prediction intervals' width can be increased (>1) or narrowed (<1)
-    # clip_predictions: False  # if data_range for respective `queries` is set in reader, `yhat.*` columns will be clipped
-    # anomaly_score_outside_data_range: 1.01  # auto anomaly score (1.01) if `y` (real value) is outside of data_range, if set
-```
-
-{{% /collapse %}}
-
-</div>
-
-Resulting metrics of the model are described [here](#vmanomaly-output).
-
-**Additional output metrics produced by Seasonal Trend Decomposition model**
- `resid` - The residual component of the data series.
- `trend` - The trend component of the data series.
- `seasonal` - The seasonal component of the data series.
-
-
 ### [Isolation forest](https://en.wikipedia.org/wiki/Isolation_forest) (Multivariate)

 > `IsolationForestModel` is a [univariate](#univariate-models), [offline](#offline-models) model.

 > `IsolationForestMultivariateModel` is a [multivariate](#multivariate-models), [offline](#offline-models) model.

+> [!NOTE]
+> Both univariate `isolation_forest` and multivariate `isolation_forest_multivariate` are planned for deprecation in a future release. For new deployments, use the corresponding univariate or multivariate online [Temporal Envelope](#temporal-envelope) model.
+
 Detects anomalies using binary trees. The algorithm has a linear time complexity and a low memory requirement, which works well with high-volume data. It can be used on both univariate and multivariate data, but it is more effective in multivariate case.

 **Important**: Be aware of [the curse of dimensionality](https://en.wikipedia.org/wiki/Curse_of_dimensionality). Don't use single multivariate model if you expect your queries to return many time series of less datapoints that the number of metrics. In such case it is hard for a model to learn meaningful dependencies from too sparse data hypercube.
@@ -1319,6 +1269,9 @@ Resulting metrics of the model are described [here](#vmanomaly-output).

 > `HoltWinters` is a [univariate](#univariate-models), [offline](#offline-models) model.

+> [!NOTE]
+> Holt-Winters is planned for deprecation in a future release. For new deployments, prefer the online [Temporal Envelope](#temporal-envelope) model.
+
 Here we use Holt-Winters Exponential Smoothing implementation from `statsmodels` [library](https://www.statsmodels.org/dev/generated/statsmodels.tsa.holtwinters.ExponentialSmoothing). All parameters from this library can be passed to the model.

 <div class="model-details">
@@ -1378,6 +1331,55 @@ models:

 Resulting metrics of the model are described [here](#vmanomaly-output).

+### [Seasonal Trend Decomposition](https://en.wikipedia.org/wiki/Seasonal_adjustment)
+
+> `StdModel` **is** {{% available_from "v1.28.0" anomaly %}} a [univariate](#univariate-models), [online](#online-models) model. It **was** {{% deprecated_from "v1.28.0" anomaly %}} a [univariate](#univariate-models), [rolling](#rolling-models), [offline](#offline-models) model.
+
+Here we use Seasonal Decompose implementation from `statsmodels` [library](https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.seasonal_decompose). Parameters from this library can be passed to the model. Some parameters are specifically predefined in `vmanomaly` and can't be changed by user (`model`='additive', `two_sided`=False).
+
+<div class="model-details">
+
+{{% collapse name="Model-specific arguments" %}}
+
+- `class` (string) - model class name `"model.std.StdModel"` (or `std` with class alias support{{% available_from "v1.13.0" anomaly %}})
+- `period` (integer) -  Number of datapoints in one season.
+- `z_threshold` (float, optional) - [standard score](https://en.wikipedia.org/wiki/Standard_score) for calculating boundaries to define anomaly score. Defaults to `2.5`.
+
+{{% /collapse %}}
+
+{{% collapse name="Configuration example" %}}
+
+
+```yaml
+models:
+  your_desired_alias_for_a_model:
+    class: "std"  # or 'model.std.StdModel' starting from v1.13.0
+    period: 2
+    # Common arguments for built-in model, if not set, default to
+    # See https://docs.victoriametrics.com/anomaly-detection/components/models/#common-args
+    #
+    # provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper']
+    # schedulers: [all scheduler aliases defined in `scheduler` section]
+    # queries: [all query aliases defined in `reader.queries` section]
+    # detection_direction: 'both'  # meaning both drops and spikes will be captured
+    # min_dev_from_expected: [0.0, 0.0]  # meaning, no minimal threshold is applied to prevent smaller anomalies
+    # scale: [1.0, 1.0]  # if needed, prediction intervals' width can be increased (>1) or narrowed (<1)
+    # clip_predictions: False  # if data_range for respective `queries` is set in reader, `yhat.*` columns will be clipped
+    # anomaly_score_outside_data_range: 1.01  # auto anomaly score (1.01) if `y` (real value) is outside of data_range, if set
+```
+
+{{% /collapse %}}
+
+</div>
+
+Resulting metrics of the model are described [here](#vmanomaly-output).
+
+**Additional output metrics produced by Seasonal Trend Decomposition model**
+- `resid` - The residual component of the data series.
+- `trend` - The trend component of the data series.
+- `seasonal` - The seasonal component of the data series.
+
+
 ## vmanomaly output

 `vmanomaly` generates model-dependent output series. Their metric names can be configured in the writer section.
@@ -1559,7 +1561,7 @@ See the [component configuration reference](https://docs.victoriametrics.com/ano
 Pull the `vmanomaly` image:

 ```sh
-docker pull victoriametrics/vmanomaly:v1.30.0
+docker pull victoriametrics/vmanomaly:v1.30.1
 ```

 Mount the module at `/vmanomaly/src/model/custom.py`, which matches the configured import path `model.custom.CustomModel`. Validate the complete configuration with `--dryRun` before starting the long-running service.
@@ -1569,7 +1571,7 @@ docker run --rm \
  -v "$PWD/license:/license:ro" \
  -v "$PWD/custom_model.py:/vmanomaly/src/model/custom.py:ro" \
  -v "$PWD/config.yaml:/config.yaml:ro" \
-  victoriametrics/vmanomaly:v1.30.0 \
+  victoriametrics/vmanomaly:v1.30.1 \
  /config.yaml \
  --licenseFile=/license \
  --dryRun
--- a/docs/anomaly-detection/components/monitoring.md
+++ b/docs/anomaly-detection/components/monitoring.md
@@ -588,7 +588,7 @@ Label names [description](#labelnames)

 `Counter`
            </td>
-            <td>The number of valid datapoints accepted by `model_alias`, excluding NaN and Inf values, during `fit`, `infer`, or combined `fit_infer` execution for the `query_key` query.</td>
+            <td>The number of valid datapoints accepted by `model_alias`, excluding NaN and Inf values, during `fit`, `infer`, or combined `fit_infer` execution for the `query_key` query. During inference, only previously unseen valid rows are counted {{% available_from "v1.30.1" anomaly %}}.</td>
            <td>

 `stage`, `query_key`, `model_alias`, `scheduler_alias`, `preset`
@@ -687,7 +687,7 @@ Label names [description](#labelnames)

 `Histogram` (was `Summary`{{% deprecated_from "v1.17.0" anomaly %}})
            </td>
-            <td>The total time (in seconds) taken by write requests to VictoriaMetrics `url` for the `query_key` query within the specified scheduler `scheduler_alias`, in the `vmanomaly` service running in `preset` mode.
+            <td>The total time (in seconds) taken by write requests to VictoriaMetrics `url` for the `query_key` query within the specified scheduler `scheduler_alias`, in the `vmanomaly` service running in `preset` mode. Successful and handled failed attempts, including connection retries, are observed {{% available_from "v1.30.1" anomaly %}}.
 </td>
            <td>

--- a/docs/anomaly-detection/components/reader.md
+++ b/docs/anomaly-detection/components/reader.md
@@ -87,7 +87,7 @@ There is change {{% available_from "v1.13.0" anomaly %}} of [`queries`](https://

 - `max_points_per_query`{{% available_from "v1.17.0" anomaly %}} (int): Optional arg, overrides how `search.maxPointsPerTimeseries` flag{{% available_from "v1.14.1" anomaly %}} impacts `vmanomaly` on splitting long `fit_window` [queries](https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader) into smaller sub-intervals. This helps users avoid hitting the `search.maxQueryDuration` limit for individual queries by distributing initial query across multiple subquery requests with minimal overhead. Set less than `search.maxPointsPerTimeseries` if hitting `maxQueryDuration` limits. If set on a query-level, it overrides the global `max_points_per_query` (reader-level).

- `tz`{{% available_from "v1.18.0" anomaly %}} (string): this optional argument enables timezone specification per query, overriding the reader’s default `tz`. This setting helps to account for local timezone shifts, such as [DST](https://en.wikipedia.org/wiki/Daylight_saving_time), in models that are sensitive to seasonal variations (e.g., [`ProphetModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet) or [`OnlineQuantileModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile)).
+- `tz`{{% available_from "v1.18.0" anomaly %}} (string): this optional argument enables timezone specification per query, overriding the reader’s default `tz`. This setting helps to account for local timezone shifts, such as [DST](https://en.wikipedia.org/wiki/Daylight_saving_time), in models that are sensitive to seasonal variations (e.g., [`TemporalEnvelopeModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) or [`OnlineQuantileModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile)).

 - `tenant_id` {{% available_from "v1.19.0" anomaly %}} (string): this optional argument enables tenant-level separation for queries (e.g. `query1` to get the data from tenant "0:0", `query2` - from tenant "1:0"). It works as follows:
  - if *not set, inherits* reader-level `tenant_id`
@@ -441,7 +441,7 @@ Optional arg{{% available_from "v1.17.0" anomaly %}} overrides how `search.maxPo
 `UTC`
            </td>
            <td>
-Optional argument {{% available_from "v1.18.0" anomaly %}} specifies the [IANA](https://nodatime.org/TimeZones) timezone to account for local shifts, like [DST](https://en.wikipedia.org/wiki/Daylight_saving_time), in models sensitive to seasonal patterns (e.g., [`TemporalEnvelopeModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope), [`ProphetModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet), or [`OnlineQuantileModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile)). Defaults to `UTC` if not set and can be overridden on a [per-query basis](#per-query-parameters).
+Optional argument {{% available_from "v1.18.0" anomaly %}} specifies the [IANA](https://nodatime.org/TimeZones) timezone to account for local shifts, like [DST](https://en.wikipedia.org/wiki/Daylight_saving_time), in models sensitive to seasonal patterns (e.g., [`TemporalEnvelopeModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) or [`OnlineQuantileModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile)). Defaults to `UTC` if not set and can be overridden on a [per-query basis](#per-query-parameters).
            </td>
        </tr>
        <tr>
@@ -825,7 +825,7 @@ Frequency of the points returned. Will be converted to `/select/stats_query_rang
 `America/New_York`
            </td>
            <td>
-(Optional) Specifies the [IANA](https://nodatime.org/TimeZones) timezone to account for local shifts, like [DST](https://en.wikipedia.org/wiki/Daylight_saving_time), in models sensitive to seasonal patterns (e.g., [`ProphetModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet) or [`OnlineQuantileModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile)). Defaults to `UTC` if not set and can be overridden on a [per-query basis](#per-query-parameters).
+(Optional) Specifies the [IANA](https://nodatime.org/TimeZones) timezone to account for local shifts, like [DST](https://en.wikipedia.org/wiki/Daylight_saving_time), in models sensitive to seasonal patterns (e.g., [`TemporalEnvelopeModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#temporal-envelope) or [`OnlineQuantileModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile)). Defaults to `UTC` if not set and can be overridden on a [per-query basis](#per-query-parameters).
            </td>
        </tr>
        <tr>
--- a/docs/anomaly-detection/components/scheduler.md
+++ b/docs/anomaly-detection/components/scheduler.md
@@ -74,6 +74,8 @@ options={`"scheduler.periodic.PeriodicScheduler"`, `"scheduler.oneoff.OneoffSche

 > {{% available_from "v1.30.0" anomaly %}} If a periodic scheduler worker exits unexpectedly, the service attempts bounded restarts with exponential backoff instead of shutting down unrelated schedulers. Monitor [`vmanomaly_scheduler_alive`](https://docs.victoriametrics.com/anomaly-detection/components/monitoring/#startup-metrics) and `vmanomaly_scheduler_restarts_total` to alert on persistent failures.

+> {{% available_from "v1.30.1" anomaly %}} For exact-capable [online models](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-models), `infer_every` is also the causal model-update cadence. If a delayed periodic job fetches several observations at once, they are processed on the same chronological grid used by exact backtesting rather than as one behaviorally different batch.
+
 ### Parameters

 For periodic scheduler parameters are defined as differences in times, expressed in difference units, e.g. days, hours, minutes, seconds. Time granularity is defined by the last characters of a string. Examples: `"50s"` (seconds), `"4m"` (minutes), `"3h"` (hours), `"2d"` (days), `"1w"` (weeks).
@@ -440,7 +442,7 @@ In **Inference only** mode {{% available_from "v1.22.1" anomaly %}}, the schedul
 - `fit_window`: Duration of historical data used for each training run (e.g. `P7D`, `PT1H`).
 - `fit_every`: Interval between consecutive training/inference cycles.
 - {{% available_from "v1.28.0" anomaly %}} `exact`: If set to `true`, BacktestingScheduler will execute inference for online models in small chronological batches equal to `infer_every` to mimic the production scheduler. (default: `false`)
- {{% available_from "v1.28.0" anomaly %}} `infer_every`: Optional inference cadence for exact mode, defining how often the scheduler should call infer between two fits, otherwise defaults to `fit_every` when unset.
+- {{% available_from "v1.28.0" anomaly %}} `infer_every`: Optional inference grid and, in exact mode, model-call cadence between two fits. {{% available_from "v1.30.1" anomaly %}} In `inference_only` mode, an omitted value is derived from the effective query step or reader sampling period and capped by `fit_every`; it falls back to `fit_every` only when neither reader value is available.
 - `n_jobs`: Number of parallel jobs for backtesting (default: `1`).

 #### Example
--- a/docs/anomaly-detection/components/server.md
+++ b/docs/anomaly-detection/components/server.md
@@ -74,5 +74,7 @@ Rest API endpoints (e.g. `/metrics`) can be accessed at `<vmanomaly-host>:8490/v
 - `GET /api/v1/autotune/tasks/{task_id}` returns progress and the concrete suggested `modelConfig` when complete.
 - `DELETE /api/v1/autotune/tasks/{task_id}` cancels pending work cooperatively.

+{{% available_from "v1.30.1" anomaly %}} Seasonality analysis preserves the original timestamp grid when samples are offset from whole step boundaries. This avoids missing daily or weekly patterns solely because timestamps are shifted within the configured sampling interval.
+
 > [!TIP]
 > For a complete request and recommended workflow, see [Shared asynchronous autotune workflow](https://docs.victoriametrics.com/anomaly-detection/components/models/#shared-asynchronous-autotune-workflow). OpenAPI schemas for the running version are available at `/docs` endpoint of a running `vmanomaly` instance.
--- a/docs/anomaly-detection/components/settings.md
+++ b/docs/anomaly-detection/components/settings.md
@@ -42,7 +42,7 @@ schedulers:
  # other schedulers

 models:
-  zscore_online_override:
+  zscore_online_inherited:
    class: zscore_online
    z_threshold: 3.5
    clip_predictions: True
@@ -73,6 +73,7 @@ reader:
 writer:
  class: "vm"
  datasource_url: http://localhost:8428
+  tenant_id: "0"
  metric_format:
    __name__: "$VAR"
    for: "$QUERY_KEY"
@@ -249,12 +250,11 @@ models:
    class: zscore_online
    z_threshold: 3.5
    schedulers: ['periodic_1d']
-  prophet:
-    class: prophet
+  temporal_envelope:
+    class: temporal_envelope
    schedulers: ['periodic_1d']
    queries: ['q1', 'q2']
-    args:
-      interval_width: 0.98
+    seasonalities: ['hod_smooth', 'dow_smooth']
 reader:
  class: vm
  datasource_url: 'https://play.victoriametrics.com'
@@ -268,7 +268,7 @@ reader:
 # other components like writer, monitoring, etc.
 ```

-if the service is restarted in less than 1 hour after the last training (now < next scheduled fit time), it will restore the state of the `zscore_online` and `prophet` models if their signature (class, hyperparameters, schedulers, etc.) has not changed. It will load the trained model instances or their training data from disk and continue producing [anomaly scores](https://docs.victoriametrics.com/anomaly-detection/faq/#what-is-anomaly-score) without retraining. If there are changes or new queries added to the configuration, the service will add these to scheduled jobs for fit and infer. That's what is changed and what is restored in a config below:
+if the service is restarted in less than 1 hour after the last training (now < next scheduled fit time), it will restore the state of the `zscore_online` and `temporal_envelope` models if their signature (class, hyperparameters, schedulers, etc.) has not changed. It will load the trained model instances or their training data from disk and continue producing [anomaly scores](https://docs.victoriametrics.com/anomaly-detection/faq/#what-is-anomaly-score) without retraining. If there are changes or new queries added to the configuration, the service will add these to scheduled jobs for fit and infer. That's what is changed and what is restored in a config below:

 ```yaml
 settings:
@@ -285,12 +285,11 @@ models:
    class: zscore_online  # unchanged, still the same model class
    z_threshold: 3.0 # changed, needs retraining!
    schedulers: ['periodic_1d']  # unchanged, still attached to the same scheduler
-  prophet:  # can be partially reused, because its class and schedulers are unchanged but queries have changed
-    class: prophet  # unchanged, still the same model class
+  temporal_envelope:  # can be partially reused, because its class and schedulers are unchanged but queries have changed
+    class: temporal_envelope  # unchanged, still the same model class
    schedulers: ['periodic_1d']  # unchanged, still attached to the same scheduler
-    queries: ['q1', 'q3']  # changed, added new query 'q3', drops 'q2', so (prophet, q2) should be trained from scratch
-    args:
-      interval_width: 0.98  # unchanged, still the same argument
+    queries: ['q1', 'q3']  # changed, added new query 'q3', drops 'q2', so (temporal_envelope, q2) should be trained from scratch
+    seasonalities: ['hod_smooth', 'dow_smooth']  # unchanged
 reader:  # can be partially reused, because its class and datasource URL are unchanged, but queries have changed
  class: vm  # unchanged, still the same reader class
  datasource_url: 'https://play.victoriametrics.com'  # unchanged, still the same datasource URL
@@ -301,13 +300,13 @@ reader:  # can be partially reused, because its class and datasource URL are unc
    q2:
      expr: 'some_metricsql_query_2'  # will be removed, no longer used by any model
    q3:
-      expr: 'some_metricsql_query_3'  # new query, added to the reader, and used by the `prophet` model
+      expr: 'some_metricsql_query_3'  # new query, added to the reader, and used by the `temporal_envelope` model
  sampling_period: 30s  # unchanged, still the same sampling period
 # other components like writer, monitoring, etc. remain unchanged
 ```
 This means that the service upon restart:
 1. Won't restore the state of `zscore_online` model, because its `z_threshold` argument **has changed**, retraining from scratch is needed on the last `fit_window` = 24 hours of data for `q1`, `q2` and `q3` (as model's `queries` arg is not set so it defaults to all queries found in the reader).
-2. Will **partially** restore the state of `prophet` model, because its class and schedulers are unchanged, but **only instances trained on timeseries returned by `q1` query**. New fit/infer jobs will be set for new query `q3`. The old query `q2` artifacts will be dropped upon restart - all respective models and data for (`prophet`, `q2`) combination will be removed from the database file and from the disk.
+2. Will **partially** restore the state of `temporal_envelope` model, because its class and schedulers are unchanged, but **only instances trained on timeseries returned by `q1` query**. New fit/infer jobs will be set for new query `q3`. The old query `q2` artifacts will be dropped upon restart - all respective models and data for (`temporal_envelope`, `q2`) combination will be removed from the database file and from the disk.

 {{% /collapse %}}

@@ -371,7 +370,7 @@ models:
    queries: ['q1']
    # other model args
  m2:  # model instances will be likely dropped during retention checks due to high churn rate
-    class: prophet
+    class: temporal_envelope
    schedulers: ['s1']
    queries: ['q2']
    # other model args
@@ -406,7 +405,7 @@ settings:
  restore_state: True  # enables state restoration
  logger_levels:
    reader.vm: DEBUG  # affects only VmReader logs
-    model: WARNING  # applies to all components with 'model' prefix, such as 'model.zscore_online', 'model.prophet', etc.
+    model: WARNING  # applies to all components with 'model' prefix, such as 'model.zscore_online', 'model.online.temporal_envelope', etc.
    # once commented out in hot-reload mode, will use the default logger level set by --loggerLevel command line argument
    # monitoring.push: critical
 ```
--- a/docs/anomaly-detection/guides/guide-vmanomaly-vmalert/README.md
+++ b/docs/anomaly-detection/guides/guide-vmanomaly-vmalert/README.md
@@ -395,7 +395,7 @@ services:
    restart: always
  vmanomaly:
    container_name: vmanomaly
-    image: victoriametrics/vmanomaly:v1.30.0
+    image: victoriametrics/vmanomaly:v1.30.1
    depends_on:
      - "victoriametrics"
    ports:
--- a/docs/guides/connecting-vm-components-to-cloud-storage/README.md
+++ b/docs/guides/connecting-vm-components-to-cloud-storage/README.md
@@ -1,3 +1,11 @@
+---
+build:
+  list: never
+  publishResources: false
+  render: never
+sitemap:
+  disable: true
+---
 Several VictoriaMetrics components can connect to cloud storage to read or write object data.

 The following table shows the supported types of storage for each component:
--- a/docs/guides/grafana-vmauth-openid-configuration/README.md
+++ b/docs/guides/grafana-vmauth-openid-configuration/README.md
@@ -1,3 +1,11 @@
+---
+build:
+  list: never
+  publishResources: false
+  render: never
+sitemap:
+  disable: true
+---
 Using [Grafana](https://grafana.com/) with [vmauth](https://docs.victoriametrics.com/victoriametrics/vmauth/) is an effective way to provide [multi-tenant](https://docs.victoriametrics.com/victoriametrics/cluster-victoriametrics/#multitenancy) access to your metrics, logs, and traces.
 vmauth provides a way to authenticate users using [JWT tokens](https://en.wikipedia.org/wiki/JSON_Web_Token) {{% available_from "v1.138.0" %}} issued by an external identity provider.
 Those tokens can include information about the user and their tenant, which vmauth can use to restrict access so users only see metrics in their own tenant.
--- a/docs/opentelemetry/README.md
+++ b/docs/opentelemetry/README.md
@@ -1,3 +1,11 @@
+---
+build:
+  list: never
+  publishResources: false
+  render: never
+sitemap:
+  disable: true
+---
 VictoriaMetrics software provides native [OpenTelemetry](https://opentelemetry.io/) ingestion across **metrics**, **logs**, and **traces** via dedicated components.
 This allows running OpenTelemetry-based observability pipeline with VictoriaMetrics software as your backend.

@@ -88,4 +96,4 @@ Depending on the Grafana datasource plugin there could be multiple correlations
 1. Trace to metrics, metric to logs, metric to traces - see [correlations via VictoriaMetrics plugin](https://docs.victoriametrics.com/victoriametrics/integrations/grafana/datasource/#correlations).
 1. Metrics to logs or traces correlations are possible via Prometheus datasource as well.
 1. Plugins Tempo, Jaeger, and Zipkin can correlate with logs or metrics using [Trace to logs](https://grafana.com/docs/grafana/latest/explore/trace-integration/#trace-to-logs)
-  and [Trace to metrics](https://grafana.com/docs/grafana/latest/visualizations/explore/trace-integration/#trace-to-metrics) feature.
+  and [Trace to metrics](https://grafana.com/docs/grafana/latest/visualizations/explore/trace-integration/#trace-to-metrics) feature.
--- a/docs/playgrounds/README.md
+++ b/docs/playgrounds/README.md
@@ -1,3 +1,11 @@
+---
+build:
+  list: never
+  publishResources: false
+  render: never
+sitemap:
+  disable: true
+---
 VictoriaMetrics offers public playgrounds where you can try the full observability stack online.

 Some playgrounds are based on the [OpenTelemetry Astronomy Shop demo](https://github.com/open-telemetry/opentelemetry-demo), a sample microservices application that generates realistic metrics, logs, and traces. Other playgrounds use benchmark workloads such as [prometheus-benchmark](https://github.com/VictoriaMetrics/prometheus-benchmark) to demonstrate ingestion and query performance for Prometheus-compatible systems.
@@ -158,4 +166,4 @@ Iximiuz Labs provides various [learning-by-doing resources for VictoriaMetrics](
  - [VictoriaMetrics cluster](https://labs.iximiuz.com/playgrounds/victoriametrics-cluster)
  - [VictoriaMetrics on Kubernetes](https://labs.iximiuz.com/playgrounds/victoriametrics-kubernetes)

-Iximiuz Labs requires a [free account](https://labs.iximiuz.com/signup) to access the materials.
+Iximiuz Labs requires a [free account](https://labs.iximiuz.com/signup) to access the materials.
--- a/docs/victoriametrics/Cluster-VictoriaMetrics.md
+++ b/docs/victoriametrics/Cluster-VictoriaMetrics.md
@@ -291,7 +291,7 @@ If you need multi-AZ setup, then it is recommended running independent clusters
 into all the cluster - see [these docs](https://docs.victoriametrics.com/victoriametrics/vmagent/#multitenancy) for details.
 Then an additional `vmselect` nodes can be configured for reading the data from multiple clusters according to [these docs](#multi-level-cluster-setup).

-See [victoria-metrics-distributed chart](https://docs.victoriametrics.com/helm/victoria-metrics-distributed/) for an example.
+See [VMDistributed](https://docs.victoriametrics.com/operator/resources/vmdistributed/) Kubernetes operator resource for an example.

 ## Cluster setup

--- a/docs/victoriametrics/README.md
+++ b/docs/victoriametrics/README.md
@@ -1306,7 +1306,7 @@ since it uses lower amounts of RAM, CPU and network bandwidth than Prometheus.
 If you use identically configured [vmagent](https://docs.victoriametrics.com/victoriametrics/vmagent/) instances for collecting the same data
 and sending it to VictoriaMetrics, then do not forget enabling [deduplication](#deduplication) at VictoriaMetrics side.

-See [victoria-metrics-distributed chart](https://docs.victoriametrics.com/helm/victoria-metrics-distributed/) for an example.
+See [VMDistributed](https://docs.victoriametrics.com/operator/resources/vmdistributed/) Kubernetes operator resource for an example.

 ## Deduplication

--- a/docs/victoriametrics/changelog/CHANGELOG.md
+++ b/docs/victoriametrics/changelog/CHANGELOG.md
@@ -26,6 +26,12 @@ See also [LTS releases](https://docs.victoriametrics.com/victoriametrics/lts-rel

 ## tip

+* FEATURE: [vmui](https://docs.victoriametrics.com/victoriametrics/single-server-victoriametrics/#vmui): add an option to customize the favicon color. This makes it easier to distinguish between different installations opened in multiple browser tabs. See [#11329](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/11329).
+
+* BUGFIX: [vmsingle](https://docs.victoriametrics.com/victoriametrics/single-server-victoriametrics/), `vmselect` in [VictoriaMetrics cluster](https://docs.victoriametrics.com/victoriametrics/cluster-victoriametrics/) and [vmctl](https://docs.victoriametrics.com/victoriametrics/vmctl/): properly parse small fractional Unix timestamps in timestamp args such as `start` and `end` in `/api/v1/query_range` and `--vm-native-filter-time-start` and `--vm-native-filter-time-end` in `vmctl`. Previously, fractional Unix timestamps with the integer part below `9223372` were interpreted with the wrong unit, for example `12.0` was parsed as `12000` seconds instead of `12` seconds. See [#11324](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/11324).
+* BUGFIX: [vmui](https://docs.victoriametrics.com/victoriametrics/single-server-victoriametrics/#vmui): respect the custom query step specified via `g0.step_input` when opening a URL. Previously, it could be reset to the automatically calculated step and potentially cause dashboards to freeze. See [#11137](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/11137).
+* BUGFIX: [vmalert](https://docs.victoriametrics.com/victoriametrics/vmalert/): properly update group-level `eval_delay` and `eval_alignment` for existing groups during runtime when config reload is triggered periodically or manually via `/-/reload`. Previously, these settings weren't updated after config reload during runtime. See [#11374](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/11374).
+
 ## [v1.149.0](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v1.149.0)

 Released at 2026-08-05
@@ -45,6 +51,7 @@ Released at 2026-08-05
 * FEATURE: [vmctl](https://docs.victoriametrics.com/victoriametrics/vmctl/): support [Prometheus native histograms](https://prometheus.io/docs/specs/native_histograms/) migration in [remote read mode](https://docs.victoriametrics.com/victoriametrics/vmctl/remoteread/). Native histograms are converted into `_count`, `_sum` and `_bucket` series with `vmrange` labels in the same way as VictoriaMetrics [converts native histograms received via Prometheus remote write protocol](https://docs.victoriametrics.com/victoriametrics/integrations/prometheus/#native-histograms), except that for native histograms with custom buckets the original bucket bounds are preserved instead of being estimated with the exponential formula. Previously native histograms were silently ignored in `SAMPLES` mode, while in stream mode the migration failed with `EOF` error. See [#11292](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/11292). Thanks to @liuxu623 for contribution.
 * FEATURE: `vminsert` in [VictoriaMetrics cluster](https://docs.victoriametrics.com/victoriametrics/cluster-victoriametrics/): enable [slowness-based re-routing](https://docs.victoriametrics.com/victoriametrics/cluster-victoriametrics/#slowness-based-re-routing) by default. Previously, `-disableRerouting` defaulted to `true`, which limited ingestion throughput to the slowest `vmstorage` node. Now `-disableRerouting` defaults to `false`, so `vminsert` automatically routes data away from the slowest `vmstorage` node, improving overall ingestion performance. Slowness re-routing is automatically disabled when `-replicationFactor` is greater than 1. See [#11287](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/11287).
 * FEATURE: [vmui](https://docs.victoriametrics.com/victoriametrics/single-server-victoriametrics/#vmui): persist the selected auto-refresh interval in the URL. See [VictoriaLogs#1310](https://github.com/VictoriaMetrics/VictoriaLogs/issues/1310).
+* FEATURE: [vmagent](https://docs.victoriametrics.com/victoriametrics/vmagent/) and [vmalert](https://docs.victoriametrics.com/victoriametrics/vmalert/): add client side least-loaded load-balancing with `DNS` discovery. See [#2388](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2388) and these [vmagent DNS URLs](https://docs.victoriametrics.com/victoriametrics/vmagent/#dns-urls), [vmalert DNS URLs](https://docs.victoriametrics.com/victoriametrics/vmalert/#dns-urls).

 * BUGFIX: [MetricsQL](https://docs.victoriametrics.com/victoriametrics/metricsql/): properly drop data points filtered out by an inner [comparison operation](https://prometheus.io/docs/prometheus/latest/querying/operators/#comparison-binary-operators) when its result is used on the right side of another comparison. Previously, queries like `foo != (bar > 100)` could return unexpected results because filtered-out data points are represented internally as `NaN`, and `value != NaN` evaluates to `true`. Comparisons against explicitly present `NaN` values keep the previous behavior. See [#10018](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/10018). Thanks to @zasdaym for contribution.
 * BUGFIX: [vmagent](https://docs.victoriametrics.com/victoriametrics/vmagent/) and [vmsingle](https://docs.victoriametrics.com/victoriametrics/single-server-victoriametrics/): ignore HTTP proxy environment variables when scraping targets over Unix domain sockets. See [#11318](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/11318). Thanks to @lwmacct for contribution.
@@ -82,7 +89,6 @@ Released at 2026-07-20
 * FEATURE: [vmagent](https://docs.victoriametrics.com/victoriametrics/vmagent/) and [vmsingle](https://docs.victoriametrics.com/victoriametrics/single-server-victoriametrics/): support scraping metrics over Unix domain sockets. The socket path can be configured via the `__unix_socket__` target label. See [#11156](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/11156). Thanks to @vinyas-bharadwaj for contribution.
 * FEATURE: [vmagent](https://docs.victoriametrics.com/victoriametrics/vmagent/) and [vmsingle](https://docs.victoriametrics.com/victoriametrics/single-server-victoriametrics/): Improve background discovery performance for [http_sd](https://docs.victoriametrics.com/victoriametrics/sd_configs/#http_sd_configs) discovery. See [#8838](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/8838).
 * FEATURE: [vmagent](https://docs.victoriametrics.com/victoriametrics/vmagent/) and [vmsingle](https://docs.victoriametrics.com/victoriametrics/single-server-victoriametrics/): allow overriding `max_scrape_size` on a per-target basis via the `__max_scrape_size__` label during target relabeling. See [#11188](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/11188).
-* FEATURE: [vmagent](https://docs.victoriametrics.com/victoriametrics/vmagent/) and [vmalert](https://docs.victoriametrics.com/victoriametrics/vmalert/): add client side least-loaded load-balancing with `DNS` discovery. See [#2388](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2388) and these [vmagent DNS URLs](https://docs.victoriametrics.com/victoriametrics/vmagent/#dns-urls), [vmalert DNS URLs](https://docs.victoriametrics.com/victoriametrics/vmalert/#dns-urls).
 * FEATURE: [vmstorage](https://docs.victoriametrics.com/victoriametrics/cluster-victoriametrics/) and [vmsingle](https://docs.victoriametrics.com/victoriametrics/single-server-victoriametrics/): add `-maxBackfillAge` command-line flag for limiting ingestion of samples with historical timestamps, for example, when older data has been moved between storage tiers (nvme/hdd, hot/cold). See [#11199](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/11199). Thanks to @AshwinRamaniPsg for contribution.
 * FEATURE: [vmagent](https://docs.victoriametrics.com/victoriametrics/vmagent/): automatically preload relabeling rules configured via `-remoteWrite.relabelConfig` and `-remoteWrite.urlRelabelConfig` in the [metrics relabel debug UI](https://docs.victoriametrics.com/victoriametrics/relabeling/#relabel-debugging). See [#9918](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/9918).

--- a/lib/timeutil/time.go
+++ b/lib/timeutil/time.go
@@ -148,11 +148,11 @@ func TryParseUnixTimestamp(s string) (int64, bool) {
 		if !ok {
 			return 0, false
 		}
-		n, ok := tryParseScientificNumberForUnixTimestamp(s[:expIdx], decimalExp)
+		n, ok := tryParseScientificUnixTimestamp(s[:expIdx], decimalExp)
 		if !ok {
 			return 0, false
 		}
-		return getUnixTimestampNanoseconds(n), true
+		return n, true
 	}

 	dotIdx := strings.IndexByte(s, '.')
@@ -168,22 +168,11 @@ func TryParseUnixTimestamp(s string) (int64, bool) {
 	// The timestamp is fractional.
 	intStr := s[:dotIdx]
 	fracStr := s[dotIdx+1:]
-	n, ok := tryParseFractionalNumberForUnixTimestamp(intStr, fracStr)
+	n, ok := tryParseFractionalUnixTimestamp(intStr, fracStr)
 	if !ok {
 		return 0, false
 	}
-
-	// Adjust the n to multiples of thousands, since this is expected by getUnixTimestampNanoseconds.
-	decimalExp := len(fracStr)
-	for decimalExp%3 != 0 {
-		if n >= 0 && n > math.MaxInt64/10 || n < 0 && n < math.MinInt64/10 {
-			return 0, false
-		}
-		n *= 10
-		decimalExp++
-	}
-
-	return getUnixTimestampNanoseconds(n), true
+	return n, true
 }

 func getExpIndex(s string) int {
@@ -196,49 +185,52 @@ func getExpIndex(s string) int {
 	return -1
 }

-func tryParseScientificNumberForUnixTimestamp(s string, decimalExp int64) (int64, bool) {
+func tryParseScientificUnixTimestamp(s string, decimalExp int64) (int64, bool) {
+	if decimalExp < 0 {
+		// Negative exponents on a fractional mantissa are intentionally not
+		// supported. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/11268
+		return 0, false
+	}
 	dotIdx := strings.IndexByte(s, '.')
 	if dotIdx < 0 {
 		n, ok := tryParseInt64(s)
 		if !ok {
 			return 0, false
 		}
-		return multiplyByDecimalExp(n, decimalExp)
-	}
-
-	if decimalExp < 0 {
-		// Negative exponents on a fractional mantissa are intentionally not
-		// supported. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/11268
-		return 0, false
+		n, ok = multiplyByDecimalExp(n, decimalExp)
+		if !ok {
+			return 0, false
+		}
+		return getUnixTimestampNanoseconds(n), true
 	}

 	intStr := s[:dotIdx]
 	fracStr := s[dotIdx+1:]
-	n, ok := tryParseFractionalNumberForUnixTimestamp(intStr, fracStr)
-	if !ok {
-		return 0, false
-	}
 	if decimalExp >= int64(len(fracStr)) {
-		// The exponent shifts the decimal point past every fractional digit,
-		// so the value is an integer number of seconds (or coarser).
+		// The exponent shifts the decimal point past every fractional digit.
+		n, ok := tryParseDecimalMantissaAsInt(intStr, fracStr)
+		if !ok {
+			return 0, false
+		}
 		decimalExp -= int64(len(fracStr))
-		return multiplyByDecimalExp(n, decimalExp)
+		n, ok = multiplyByDecimalExp(n, decimalExp)
+		if !ok {
+			return 0, false
+		}
+		return getUnixTimestampNanoseconds(n), true
 	}

 	// The exponent leaves fractional digits, e.g. 1.784144612388E9 == 1784144612.388
-	// Pad n as plain fractional timestamps do.
-	fracDigits := int64(len(fracStr)) - decimalExp
-	for fracDigits%3 != 0 {
-		if n >= 0 && n > math.MaxInt64/10 || n < 0 && n < math.MinInt64/10 {
-			return 0, false
-		}
-		n *= 10
-		fracDigits++
+	if decimalExp >= int64(len(decimalMultipliers)) {
+		return 0, false
 	}
-	return n, true
+	decimalExpInt := int(decimalExp)
+	intStr = s[:dotIdx] + fracStr[:decimalExpInt]
+	fracStr = fracStr[decimalExpInt:]
+	return tryParseFractionalUnixTimestamp(intStr, fracStr)
 }

-func tryParseFractionalNumberForUnixTimestamp(intStr, fracStr string) (int64, bool) {
+func tryParseDecimalMantissaAsInt(intStr, fracStr string) (int64, bool) {
 	n, ok := tryParseInt64(intStr)
 	if !ok {
 		return 0, false
@@ -270,6 +262,53 @@ func tryParseFractionalNumberForUnixTimestamp(intStr, fracStr string) (int64, bo
 	return num, true
 }

+func tryParseFractionalUnixTimestamp(intStr, fracStr string) (int64, bool) {
+	n, ok := tryParseInt64(intStr)
+	if !ok {
+		return 0, false
+	}
+	isNegative := n < 0 || n == 0 && strings.HasPrefix(intStr, "-")
+
+	multiplier, maxFracDigits := getUnixTimestampMultiplier(n)
+	// Truncate the fractional digits to valid length according to the unit precision.
+	if len(fracStr) > maxFracDigits {
+		// 1.123456789XXX is invalid.
+		tail := fracStr[maxFracDigits:]
+		for i := 0; i < len(tail); i++ {
+			if tail[i] < '0' || tail[i] > '9' {
+				return 0, false
+			}
+		}
+		fracStr = fracStr[:maxFracDigits]
+	}
+	if len(fracStr) == 0 {
+		return n * multiplier, true
+	}
+
+	frac, ok := tryParseInt64(fracStr)
+	if !ok {
+		return 0, false
+	}
+	decimalExp := len(fracStr)
+	if decimalExp >= len(decimalMultipliers) {
+		return 0, false
+	}
+	n *= multiplier
+	scale := decimalMultipliers[decimalExp]
+	frac *= multiplier / scale
+
+	if isNegative {
+		if n < math.MinInt64+frac {
+			return 0, false
+		}
+		return n - frac, true
+	}
+	if n > math.MaxInt64-frac {
+		return 0, false
+	}
+	return n + frac, true
+}
+
 func multiplyByDecimalExp(n int64, decimalExp int64) (int64, bool) {
 	if decimalExp < 0 {
 		return 0, false
@@ -302,20 +341,25 @@ const (
 )

 func getUnixTimestampNanoseconds(n int64) int64 {
+	multiplier, _ := getUnixTimestampMultiplier(n)
+	return n * multiplier
+}
+
+func getUnixTimestampMultiplier(n int64) (int64, int) {
 	if n <= maxValidSecond && n >= minValidSecond {
 		// The timestamp is in seconds.
-		return n * 1e9
+		return 1e9, 9
 	}
 	if n <= maxValidMilli && n >= minValidMilli {
 		// The timestamp is in milliseconds.
-		return n * 1e6
+		return 1e6, 6
 	}
 	if n <= maxValidMicro && n >= minValidMicro {
 		// The timestamp is in microseconds.
-		return n * 1e3
+		return 1e3, 3
 	}
 	// The timestamp is in nanoseconds
-	return n
+	return 1, 0
 }

 func tryParseInt64(s string) (int64, bool) {
--- a/lib/timeutil/time_test.go
+++ b/lib/timeutil/time_test.go
@@ -27,11 +27,17 @@ func TestTryParseUnixTimestamp_Success(t *testing.T) {
 	f("-1234567890123456789", -1234567890_123_456_789)
 	f("1234567890123456789", 1234567890_123_456_789)
 	f("1234567890123456.789", 1234567890_123_456_789)
+	f("12345678901234567.891", 12345678_901_234_567)
+	f("12345678901234567.89", 12345678_901_234_567)
+	f("12345678901234567.8", 12345678_901_234_567)

 	// microseconds
 	f("-1234567890123456", -1234567890_123_456_000)
 	f("1234567890123456", 1234567890_123_456_000)
 	f("1234567890123456.789", 1234567890_123_456_789)
+	f("12345678901234.5000", 12345678_901_234_500)
+	f("12345678901234.5123", 12345678_901_234_512)
+	f("12345678901234.567891", 12345678_901_234_567)

 	// milliseconds
 	f("-1234567890123", -1234567890_123_000_000)
@@ -50,6 +56,19 @@ func TestTryParseUnixTimestamp_Success(t *testing.T) {
 	f("-1234567890.123", -1234567890_123_000_000)
 	f("-1234567890.12", -1234567890_120_000_000)
 	f("-1234567890.1", -1234567890_100_000_000)
+	f("12", 12_000_000_000)
+	f("12.", 12_000_000_000)
+	f("12.0", 12_000_000_000)
+	f("12.34", 12_340_000_000)
+	f("12.999999999000000000", 12_999_999_999)
+	f("0.1234567890123456789123", 123_456_789)
+	f("-0.1234567890123456789123", -123_456_789)
+	f("-12", -12_000_000_000)
+	f("-12.34", -12_340_000_000)
+	f("8223372", 8223372_000_000_000)
+	f("8223372.0", 8223372_000_000_000)
+	f("1700000000", 1700000000_000_000_000)
+	f("1700000000.0", 1700000000_000_000_000)

 	// scientific notation
 	f("1e9", 1000000000_000_000_000)
@@ -78,8 +97,10 @@ func TestTryParseUnixTimestamp_Success(t *testing.T) {
 	f("-1.784144612388e9", -1784144612_388_000_000)
 	f("1.5000000005e9", 1500000000_500_000_000) // == 1500000000.5
 	f("1.23456789e9", 1234567890_000_000_000)   // exponent consumes all frac digits (integer result)
-	f("1.23e1", 12300_000_000_000)              // == 12.3
-	f("1.234e0", 1234_000_000_000)              // == 1.234
+	f("1.23e1", 12_300_000_000)                 // == 12.3
+	f("1.234e0", 1_234_000_000)                 // == 1.234
+	f("1234567890123456789.0e0", 1234567890_123_456_789)
+
 }

 func TestTryParseUnixTimestamp_Failure(t *testing.T) {
@@ -97,30 +118,20 @@ func TestTryParseUnixTimestamp_Failure(t *testing.T) {
 	f("foobar")
 	f("foo.bar")
 	f("1.12345671x34")
+	f("1.123456789x")
 	f("1.3e12345678x0123")
 	f("1xs.12345671")
 	f("1xs.12345671e5")
 	f("-1xs.12345671e5")

-	// missing fractional part
-	f("1233344.")
-
 	// too big timestamp
-	f("12345678901234567.891")
 	f("12345678901234567890")
-	f("12345678901234.567891")
 	f("12345678901234567890e3")
 	f("12345678901234567890.234e3")
 	f("-12345678901234567890")
 	f("12345678901234567890.235424")
 	f("12345678901234567890.235424e3")
 	f("-12345678901234567890.235424")
-	f("12345678901234567.89")
-	f("12345678901234567.8")
-
-	// too big fractional part
-	f("0.1234567890123456789123")
-	f("-0.1234567890123456789123")

 	// too big decimal exponent
 	f("1e19")
@@ -129,6 +140,7 @@ func TestTryParseUnixTimestamp_Failure(t *testing.T) {
 	// negative decimal exponent
 	f("1E-1")
 	f("1.3e-123456789090123")
+
 }

 func TestParseTimeAtSuccess(t *testing.T) {
Author	SHA1	Message	Date
JAYICE	bfeb5ce27a	Merge branch 'master' into issue-11374 Signed-off-by: JAYICE <1185430411@qq.com>	2026-08-11 13:44:02 +08:00
Yury Moladau	8a3757d21b	app/vmui: fix custom step synchronization between state and URL (#11350 ) ### Describe Your Changes Fix custom query step synchronization in vmui. Previously, the custom step specified via the `g0.step_input` URL parameter could be overwritten by the automatically calculated step during page initialization. This could also potentially cause the vmui Dashboards page to freeze. Related issue: #11137 Signed-off-by: Yury Molodov <yurymolodov@gmail.com> Co-authored-by: Max Kotliar <mkotlyar@victoriametrics.com>	2026-08-10 19:29:10 +03:00
Yury Moladau	b6952cf346	app/vmui: add an option to customize the favicon color (#11331 ) Make VictoriaMetrics instances easier to distinguish by allowing users to customize the favicon color. This PR also refreshes the Settings modal layout and controls. Mirror of https://github.com/VictoriaMetrics/VictoriaLogs/pull/1646 Related issue: https://github.com/VictoriaMetrics/VictoriaMetrics/pull/1634 <img width="497" height="47" alt="image" src="https://github.com/user-attachments/assets/a250fbb1-b21a-4425-92f7-3aee6dc78a2b" /> Changes: - add per-instance favicon color customization with 12 predefined colors and a reset option - persist the selected color in `localStorage` and synchronize it across browser tabs - refresh the Settings modal layout and controls ### Browser compatibility Safari keeps the default favicon because it doesn't support data URL favicons. ### Screenshots \| Before \| After \| \|---\|---\| \| <img width="643" height="726" alt="image" src="https://github.com/user-attachments/assets/4fda4423-011f-4721-bf21-355135d62a80" /> \| <img width="643" height="726" alt="image" src="https://github.com/user-attachments/assets/2eee5767-bcad-4e06-85bc-e74704d0f660" /> \| Signed-off-by: Yury Molodov <yurymolodov@gmail.com> Co-authored-by: Max Kotliar <mkotlyar@victoriametrics.com>	2026-08-10 19:22:11 +03:00
Andrii Chubatiuk	d142a1682f	docs: replace distributed chart links with VMDistributed CR (#11364 ) replaced links to deprecated distributed chart with ones to VMDistributed resource	2026-08-10 14:38:51 +03:00
Hui Wang	bcb653611c	docs/changelog: fix misplaced changelog entry location (#11373 )	2026-08-10 14:28:01 +03:00
Pablo (Tomas) Fernandez	6cc9a6a2f3	docs: add missing frontmatter in READMEs. Remove duplicate URLs (#11367 ) Some README.md do not have a frontmatter. This leads to duplicated URLs when published. For example, these two URLs render the same page, which leads to duplication. The first one should return 404. - https://docs.victoriametrics.com/opentelemetry/readme/index.html - https://docs.victoriametrics.com/opentelemetry/ This PR adds missing frontmatter so all duplicate URLs are removed and never rendered. ```yaml --- build: list: never publishResources: false render: never sitemap: disable: true --- ``` (issue reported by @hagen1778, thank you!)	2026-08-10 14:25:35 +03:00
“Jayice”	88a94bf84c	properly update `eval_delay` and `eval_alignment` for existing groups after hot config reload Signed-off-by: “Jayice” <jzhou@victoriametrics.com>	2026-08-10 13:45:04 +08:00
Fred Navruzov	c620cb30b8	docs: update vmanomaly for v1.30.1 (#11363 ) ## Summary - Updated examples to prefer online models. - Marked offline models for future deprecation. - Updated vmanomaly documentation and deployment references for v1.30.1.	2026-08-06 17:57:56 +03:00
JAYICE	0033834d3c	lib/timeutil: properly parse small unix timestamps with fractional part (#11335 ) Fix timeutil.TryParseUnixTimestamp returing different results for equivalent integer and fractional timestamps, such as `12` and `12.0`. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/11324 Signed-off-by: “Jayice” <jzhou@victoriametrics.com>	2026-08-06 15:28:42 +02:00
				`@@ -1 +0,0 @@`
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