# datamllab/tods

TODS: An Automated Time-series Outlier Detection System

Repository: https://github.com/datamllab/tods
Canonical: https://ross.abutalabs.com/products/tods
Homepage: http://tods-doc.github.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: outlier-detection, automl, anomaly-detection, machine-learning, time-series, time-series-anomaly-detection, time-series-analysis
Last push: 2023-09-11T15:48:11+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2185, "days_push": 1087, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1666, forks 206 (observed 2026-08-28T04:05:19.584470+00:00)

## What it is
TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data, developed by DATA Lab at Rice University. It provides modules for preprocessing, feature extraction, a wide range of detection algorithms (point-wise, pattern-wise, system-wise), and AutoML pipeline search.

## Use cases
- detect anomalies in multivariate time-series sensor data
- automatically build an outlier detection pipeline with AutoML
- find anomalous subsequences in time series
- detect fraudulent transactions in time-series data
- monitor blockchain data for outliers
- apply PyOD point-wise detectors to time series

## When to choose
- you need automated, knowledge-free anomaly detection pipelines for time-series data
- you want access to many detection algorithms (PyOD, DeepLog, Telemanom) in one package
- you need point-wise, pattern-wise, or system-wise outlier detection scenarios

## When to avoid
- you need real-time streaming anomaly detection at low latency
- you work outside Python or need actively maintained software with frequent updates
- your data is not time-series (use PyOD or general anomaly detection tools instead)

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, data-science, etl
- domain: machine-learning, data-science, time-series, analytics
- platform: python, windows
- tags: anomaly-detection, outlier-detection, automl, time-series, automl, human-in-the-loop, linux, macos

## Member repositories
- datamllab/tods (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:19.584470+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:42:58.681465+00:00, confidence not recorded.
  - readme: https://github.com/datamllab/tods (fetched 2026-08-28T04:05:19.584470+00:00, sha 25a0b3ca8af4)
  - homepage: http://tods-doc.github.io (fetched 2026-08-29T11:16:21.424633+00:00, sha 63be18ac69ab)
- Data as of 2026-08-30T08:39:29.467469+00:00.