# sktime/sktime

A unified framework for machine learning with time series

Repository: https://github.com/sktime/sktime
Canonical: https://ross.abutalabs.com/products/sktime
Homepage: https://www.sktime.net
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: time-series, machine-learning, scikit-learn, time-series-classification, time-series-regression, forecasting, time-series-analysis, data-science, data-mining, hacktoberfest, ai, anomaly-detection, changepoint-detection, sktime, time-series-segmentation
Last push: 2026-08-26T16:25:05+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 95, longevity 100
- inputs: {"age_days": 2857, "days_push": 7, "days_rel": 36, "gap_med": 9.5, "n_releases_24m": 25}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9965, forks 2306 (observed 2026-08-28T04:10:39.022706+00:00)

## What it is
sktime is a unified Python framework for machine learning with time series, offering 500+ models behind a single scikit-learn-compatible API. It covers forecasting, classification, regression, clustering, anomaly and changepoint detection, transformations, pipelines, and benchmarking.

## Use cases
- forecast product demand per store
- predict energy load to balance the grid
- detect anomalies in sensor data for predictive maintenance
- classify time series from wearable devices
- cluster similar time series
- detect changepoints in streaming metrics
- build and tune forecasting pipelines with AutoML
- benchmark forecasting models

## When to choose
- you need one consistent API across many time series tasks and models
- you want scikit-learn-style fit/predict workflows for forecasting or classification
- you need pipelines, tuning, ensembling, and reduction for time series
- you want to swap between classical statistics, ML, and deep learning models without rewriting code

## When to avoid
- you need distributed or out-of-core computation on very large datasets
- you need a GUI or CLI rather than a Python library
- you work outside time series domains
- you need real-time streaming ingestion rather than in-memory pandas/NumPy data

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, data-science, analytics, benchmarking
- domain: time-series, machine-learning, data-science
- platform: python, cross-platform, windows
- tags: time-series, forecasting, scikit-learn, anomaly-detection, changepoint-detection, time-series-classification, time-series-clustering, pipelines, macos, linux

## Member repositories
- sktime/sktime (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:39.022706+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 2026-08-29T17:20:03.799070+00:00, confidence not recorded.
  - readme: https://github.com/sktime/sktime (fetched 2026-08-28T04:10:39.022706+00:00, sha dbc521bdf8b1)
  - homepage: https://www.sktime.net (fetched 2026-08-29T08:20:05.923591+00:00, sha 1e606c543fd3)
  - site_page: https://www.sktime.net/docs/changelog (fetched 2026-08-29T08:20:05.939992+00:00, sha e07a310b57d4)
  - site_page: https://www.sktime.net/about (fetched 2026-08-29T08:20:05.943634+00:00, sha 082ae8d1e0fa)
  - site_page: https://www.sktime.net/docs (fetched 2026-08-29T08:20:05.935683+00:00, sha 3024ca82b57b)
  - site_page: https://www.sktime.net/docs/get-started (fetched 2026-08-29T08:20:05.933357+00:00, sha 73d9a918453c)
  - site_page: https://www.sktime.net/docs/api-reference (fetched 2026-08-29T08:20:05.937682+00:00, sha 7cc64e83e730)
  - site_page: https://www.sktime.net/docs/installation (fetched 2026-08-29T08:20:05.945900+00:00, sha 3ae6f9915fbe)
  - site_page: https://www.sktime.net/docs/examples (fetched 2026-08-29T08:20:05.948004+00:00, sha 0e213433e8f3)
  - registry_pypi: https://pypi.org/pypi/sktime/json (fetched 2026-08-29T08:20:05.949965+00:00, sha 092f1c3e591f)
- Data as of 2026-08-30T08:39:29.467469+00:00.