BranchesTags

Repository files navigation

Anomaly Detection Toolkit (ADTK)

Build StatusDocumentation StatusCoverage StatusPyPIDownloadsCode style: blackBinder

Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised / rule-based time series anomaly detection.

As the nature of anomaly varies over different cases, a model may not work universally for all anomaly detection problems. Choosing and combining detection algorithms (detectors), feature engineering methods (transformers), and ensemble methods (aggregators) properly is the key to build an effective anomaly detection model.

This package offers a set of common detectors, transformers and aggregators with unified APIs, as well as pipe classes that connect them together into models. It also provides some functions to process and visualize time series and anomaly events.

See https://adtk.readthedocs.io for complete documentation.

Installation

Prerequisites: Python 3.5 or later.

It is recommended to install the most recent stable release of ADTK from PyPI.

pip install adtk

Alternatively, you could install from source code. This will give you the latest, but unstable, version of ADTK.

git clone https://github.com/arundo/adtk.git
cd adtk/
git checkout develop
pip install ./

Examples

Please see Quick Start for a simple example.

For more detailed examples of each module of ADTK, please refer to Examples section in the documentation or an interactive demo notebook.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update unit tests as appropriate.

Please see Contributing for more details.

License

ADTK is licensed under the Mozilla Public License 2.0 (MPL 2.0). See the LICENSE file for details.

About

A Python toolkit for rule-based/unsupervised anomaly detection in time series

adtk.readthedocs.io

Topics

anomaly-detectiontime-series

Resources

Readme
MPL-2.0 license
Activity
Custom properties

Stars

1.2k stars

Watchers

23 watching

Forks

163 forks
Report repository

Releases

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Anomaly Detection Toolkit (ADTK)
Build StatusDocumentation StatusCoverage StatusPyPIDownloadsCode style: blackBinder
Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised /
rule-based time series anomaly detection.
As the nature of anomaly varies over different cases, a model may not work
universally for all anomaly detection problems. Choosing and combining
detection algorithms (detectors), feature engineering methods (transformers),
and ensemble methods (aggregators) properly is the key to build an effective
anomaly detection model.
This package offers a set of common detectors, transformers and aggregators
with unified APIs, as well as pipe classes that connect them together into
models. It also provides some functions to process and visualize time series
and anomaly events.
See https://adtk.readthedocs.io for complete documentation.
Installation
Prerequisites: Python 3.5 or later.
It is recommended to install the most recent stable release of ADTK from PyPI.
pip install adtk
Alternatively, you could install from source code. This will give you the latest, but unstable, version of ADTK.
git clone https://github.com/arundo/adtk.git
cd adtk/
git checkout develop
pip install ./
Examples
Please see Quick Start for a simple example.
For more detailed examples of each module of ADTK, please refer to
Examples
section in the documentation or an interactive demo notebook.
Contributing
Pull requests are welcome. For major changes, please open an issue first to
discuss what you would like to change.
Please make sure to update unit tests as appropriate.
Please see Contributing for more details.
License
ADTK is licensed under the Mozilla Public License 2.0 (MPL 2.0). See the
LICENSE file for details.
About
A Python toolkit for rule-based/unsupervised anomaly detection in time series
adtk.readthedocs.io
Topics
anomaly-detectiontime-series
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
1.2k stars
Watchers
23 watching
Forks
163 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Anomaly Detection Toolkit (ADTK)
Build StatusDocumentation StatusCoverage StatusPyPIDownloadsCode style: blackBinder
Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised /
rule-based time series anomaly detection.
As the nature of anomaly varies over different cases, a model may not work
universally for all anomaly detection problems. Choosing and combining
detection algorithms (detectors), feature engineering methods (transformers),
and ensemble methods (aggregators) properly is the key to build an effective
anomaly detection model.
This package offers a set of common detectors, transformers and aggregators
with unified APIs, as well as pipe classes that connect them together into
models. It also provides some functions to process and visualize time series
and anomaly events.
See https://adtk.readthedocs.io for complete documentation.
Installation
Prerequisites: Python 3.5 or later.
It is recommended to install the most recent stable release of ADTK from PyPI.
pip install adtk
Alternatively, you could install from source code. This will give you the latest, but unstable, version of ADTK.
git clone https://github.com/arundo/adtk.git
cd adtk/
git checkout develop
pip install ./
Examples
Please see Quick Start for a simple example.
For more detailed examples of each module of ADTK, please refer to
Examples
section in the documentation or an interactive demo notebook.
Contributing
Pull requests are welcome. For major changes, please open an issue first to
discuss what you would like to change.
Please make sure to update unit tests as appropriate.
Please see Contributing for more details.
License
ADTK is licensed under the Mozilla Public License 2.0 (MPL 2.0). See the
LICENSE file for details.
About
A Python toolkit for rule-based/unsupervised anomaly detection in time series
adtk.readthedocs.io
Topics
anomaly-detectiontime-series
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
1.2k stars
Watchers
23 watching
Forks
163 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Anomaly Detection Toolkit (ADTK)
Build StatusDocumentation StatusCoverage StatusPyPIDownloadsCode style: blackBinder
Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised /
rule-based time series anomaly detection.
As the nature of anomaly varies over different cases, a model may not work
universally for all anomaly detection problems. Choosing and combining
detection algorithms (detectors), feature engineering methods (transformers),
and ensemble methods (aggregators) properly is the key to build an effective
anomaly detection model.
This package offers a set of common detectors, transformers and aggregators
with unified APIs, as well as pipe classes that connect them together into
models. It also provides some functions to process and visualize time series
and anomaly events.
See https://adtk.readthedocs.io for complete documentation.
Installation
Prerequisites: Python 3.5 or later.
It is recommended to install the most recent stable release of ADTK from PyPI.
pip install adtk
Alternatively, you could install from source code. This will give you the latest, but unstable, version of ADTK.
git clone https://github.com/arundo/adtk.git
cd adtk/
git checkout develop
pip install ./
Examples
Please see Quick Start for a simple example.
For more detailed examples of each module of ADTK, please refer to
Examples
section in the documentation or an interactive demo notebook.
Contributing
Pull requests are welcome. For major changes, please open an issue first to
discuss what you would like to change.
Please make sure to update unit tests as appropriate.
Please see Contributing for more details.
License
ADTK is licensed under the Mozilla Public License 2.0 (MPL 2.0). See the
LICENSE file for details.
About
A Python toolkit for rule-based/unsupervised anomaly detection in time series
adtk.readthedocs.io
Topics
anomaly-detectiontime-series
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
1.2k stars
Watchers
23 watching
Forks
163 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Anomaly Detection Toolkit (ADTK)
Build StatusDocumentation StatusCoverage StatusPyPIDownloadsCode style: blackBinder
Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised /
rule-based time series anomaly detection.
As the nature of anomaly varies over different cases, a model may not work
universally for all anomaly detection problems. Choosing and combining
detection algorithms (detectors), feature engineering methods (transformers),
and ensemble methods (aggregators) properly is the key to build an effective
anomaly detection model.
This package offers a set of common detectors, transformers and aggregators
with unified APIs, as well as pipe classes that connect them together into
models. It also provides some functions to process and visualize time series
and anomaly events.
See https://adtk.readthedocs.io for complete documentation.
Installation
Prerequisites: Python 3.5 or later.
It is recommended to install the most recent stable release of ADTK from PyPI.
pip install adtk
Alternatively, you could install from source code. This will give you the latest, but unstable, version of ADTK.
git clone https://github.com/arundo/adtk.git
cd adtk/
git checkout develop
pip install ./
Examples
Please see Quick Start for a simple example.
For more detailed examples of each module of ADTK, please refer to
Examples
section in the documentation or an interactive demo notebook.
Contributing
Pull requests are welcome. For major changes, please open an issue first to
discuss what you would like to change.
Please make sure to update unit tests as appropriate.
Please see Contributing for more details.
License
ADTK is licensed under the Mozilla Public License 2.0 (MPL 2.0). See the
LICENSE file for details.
About
A Python toolkit for rule-based/unsupervised anomaly detection in time series
adtk.readthedocs.io
Topics
anomaly-detectiontime-series
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
1.2k stars
Watchers
23 watching
Forks
163 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Anomaly Detection Toolkit (ADTK)
Build StatusDocumentation StatusCoverage StatusPyPIDownloadsCode style: blackBinder
Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised /
rule-based time series anomaly detection.
As the nature of anomaly varies over different cases, a model may not work
universally for all anomaly detection problems. Choosing and combining
detection algorithms (detectors), feature engineering methods (transformers),
and ensemble methods (aggregators) properly is the key to build an effective
anomaly detection model.
This package offers a set of common detectors, transformers and aggregators
with unified APIs, as well as pipe classes that connect them together into
models. It also provides some functions to process and visualize time series
and anomaly events.
See https://adtk.readthedocs.io for complete documentation.
Installation
Prerequisites: Python 3.5 or later.
It is recommended to install the most recent stable release of ADTK from PyPI.
pip install adtk
Alternatively, you could install from source code. This will give you the latest, but unstable, version of ADTK.
git clone https://github.com/arundo/adtk.git
cd adtk/
git checkout develop
pip install ./
Examples
Please see Quick Start for a simple example.
For more detailed examples of each module of ADTK, please refer to
Examples
section in the documentation or an interactive demo notebook.
Contributing
Pull requests are welcome. For major changes, please open an issue first to
discuss what you would like to change.
Please make sure to update unit tests as appropriate.
Please see Contributing for more details.
License
ADTK is licensed under the Mozilla Public License 2.0 (MPL 2.0). See the
LICENSE file for details.
About
A Python toolkit for rule-based/unsupervised anomaly detection in time series
adtk.readthedocs.io
Topics
anomaly-detectiontime-series
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
1.2k stars
Watchers
23 watching
Forks
163 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Anomaly Detection Toolkit (ADTK)
Build StatusDocumentation StatusCoverage StatusPyPIDownloadsCode style: blackBinder
Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised /
rule-based time series anomaly detection.
As the nature of anomaly varies over different cases, a model may not work
universally for all anomaly detection problems. Choosing and combining
detection algorithms (detectors), feature engineering methods (transformers),
and ensemble methods (aggregators) properly is the key to build an effective
anomaly detection model.
This package offers a set of common detectors, transformers and aggregators
with unified APIs, as well as pipe classes that connect them together into
models. It also provides some functions to process and visualize time series
and anomaly events.
See https://adtk.readthedocs.io for complete documentation.
Installation
Prerequisites: Python 3.5 or later.
It is recommended to install the most recent stable release of ADTK from PyPI.
pip install adtk
Alternatively, you could install from source code. This will give you the latest, but unstable, version of ADTK.
git clone https://github.com/arundo/adtk.git
cd adtk/
git checkout develop
pip install ./
Examples
Please see Quick Start for a simple example.
For more detailed examples of each module of ADTK, please refer to
Examples
section in the documentation or an interactive demo notebook.
Contributing
Pull requests are welcome. For major changes, please open an issue first to
discuss what you would like to change.
Please make sure to update unit tests as appropriate.
Please see Contributing for more details.
License
ADTK is licensed under the Mozilla Public License 2.0 (MPL 2.0). See the
LICENSE file for details.
About
A Python toolkit for rule-based/unsupervised anomaly detection in time series
adtk.readthedocs.io
Topics
anomaly-detectiontime-series
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
1.2k stars
Watchers
23 watching
Forks
163 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Anomaly Detection Toolkit (ADTK)
Build StatusDocumentation StatusCoverage StatusPyPIDownloadsCode style: blackBinder
Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised /
rule-based time series anomaly detection.
As the nature of anomaly varies over different cases, a model may not work
universally for all anomaly detection problems. Choosing and combining
detection algorithms (detectors), feature engineering methods (transformers),
and ensemble methods (aggregators) properly is the key to build an effective
anomaly detection model.
This package offers a set of common detectors, transformers and aggregators
with unified APIs, as well as pipe classes that connect them together into
models. It also provides some functions to process and visualize time series
and anomaly events.
See https://adtk.readthedocs.io for complete documentation.
Installation
Prerequisites: Python 3.5 or later.
It is recommended to install the most recent stable release of ADTK from PyPI.
pip install adtk
Alternatively, you could install from source code. This will give you the latest, but unstable, version of ADTK.
git clone https://github.com/arundo/adtk.git
cd adtk/
git checkout develop
pip install ./
Examples
Please see Quick Start for a simple example.
For more detailed examples of each module of ADTK, please refer to
Examples
section in the documentation or an interactive demo notebook.
Contributing
Pull requests are welcome. For major changes, please open an issue first to
discuss what you would like to change.
Please make sure to update unit tests as appropriate.
Please see Contributing for more details.
License
ADTK is licensed under the Mozilla Public License 2.0 (MPL 2.0). See the
LICENSE file for details.
About
A Python toolkit for rule-based/unsupervised anomaly detection in time series
adtk.readthedocs.io
Topics
anomaly-detectiontime-series
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
1.2k stars
Watchers
23 watching
Forks
163 forks
Report repository
Releases
Used by
Contributors
Languages