adtk
A package for unsupervised time series anomaly detection
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.5 or later; depends on numpy, pandas, scikit-learn, and statsmodels being installed.
- 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 · maintenance · safety
copyleft license (copyleft) — 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.
last release 2020-04-17 (2310 days) · last repo commit 2024-08-01 · 1,213 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 522,859 downloads/mo, #6,199 on PyPI
Alternatives
Verify before relying
pip install adtk
from adtk.detector import ThresholdAD
import pandas as pd
detector = ThresholdAD(high=3.0)
anomalies = detector.detect(time_series)- 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.
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 on it.
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
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.
Requires Python 3.5 or later; depends on numpy, pandas, scikit-learn, and statsmodels being installed.
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)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 | copyleft license copyleft |
| Python support | Supports the current Python release >=3.5 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesnumpypandasmatplotlibscikit-learnstatsmodelspackagingtabulate |
| 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 |
| Classifiers | 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 |
Evidence: adtk-0.6.2-py3-none-any.whl
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