skillfed

adtk

A package for unsupervised time series anomaly detection

adtk v0.6.2 522.9K downloads/30d#6,199 on PyPI1,213
Copyleft license Mozilla Public License 2.0 (MPL 2.0) DORMANT released

What it is and what it does

ADTK is a Python library for detecting anomalies in time series data using unsupervised and rule-based methods. It provides a collection of detector algorithms, feature transformers, and ensemble aggregators that can be combined via pipe classes into complete anomaly detection models. The package also includes utilities for time series visualization and anomaly event processing.

The library is designed around the principle that no single anomaly detection algorithm works universally—instead, it offers building blocks with unified APIs so you can select and combine the right detectors, transformers, and aggregators for your specific problem. It depends on numpy, pandas, scikit-learn, statsmodels, and matplotlib for its core functionality.

Use it for:

  • Detect sudden spikes or drops in sensor data or system metrics without labeled training data.
  • Identify unusual patterns in financial time series using statistical rules.
  • Flag anomalous behavior in network traffic or application performance logs.
  • Combine multiple detection algorithms to improve sensitivity and reduce false positives in critical monitoring.
  • Visualize detected anomalies alongside raw time series data for manual review and validation.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

ADTK provides detectors, transformers, and aggregators for unsupervised and rule-based time series anomaly detection, with unified APIs and pipe classes to combine them into detection models.

Yes, if you need unsupervised time series anomaly detection and can accept dormant maintenance. The package is stable (Production/Stable status), has no known vulnerabilities, and low install friction. However, the last release was 2020-04-17; evaluate whether the detectors and dependencies meet your current needs before committing to it in new projects.

Install

adtk on PyPI

pip

pip install adtk

uv

uv add adtk

poetry

poetry add adtk

Installing adtk

Before you install

Low install friction with a pure-Python wheel and well-established dependencies. However, the package is dormant—last release was 2020-04-17, with no recent maintenance activity despite an active repository.

License in practice

Licensed under Mozilla Public License 2.0 (MPL 2.0), a copyleft license requiring derivative works and modifications to be distributed under the same license. Review your project's license compatibility before use.

Quickstart

pip install adtk

from adtk.detector import ThresholdAD
import pandas as pd

detector = ThresholdAD(high=3.0)
anomalies = detector.detect(time_series)

Requires Python 3.5 or later; depends on numpy, pandas, scikit-learn, and statsmodels being installed.

Verify before relying

  • Whether the package's detectors and transformers remain effective on modern time series data given dormant maintenance.
  • Compatibility with Python versions beyond 3.8, which was the latest tested version at release.

Package facts

License Mozilla Public License 2.0 (MPL 2.0) (copyleft)
Python support supports the current Python release (>=3.5)
Install friction low — pure-Python wheel
Runtime dependencies 7 — numpy, pandas, matplotlib, scikit-learn, statsmodels, packaging, tabulate
Maintenance dormant — 2,310 days since the last release
Last repo commit
First released
Downloads 522,859/month — #6,199 on PyPI (30-day window, as of 2026-08-14)
Known vulnerabilities none known (OSV.dev, checked 2026-08-14)

Evidence: adtk-0.6.2-py3-none-any.whl

Keywords: anomaly detection, time series

Development Status :: 5 - Production/StableLicense :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)Operating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxOperating System :: UnixProgramming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Topic :: Scientific/Engineering

Tags

time series anomaly detectionunsupervised anomaly detectionanomaly detection toolkittime series outlier detectionrule-based anomaly detectionanomaly detection pipelinestatistical anomaly detection
time-seriesanomaly-detectionunsupervised-learning

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