#5252nd of 75 in ML frameworks and librariesFor data and documents
sktime
Python library with one scikit-learn-style API for time series tasks
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- Stars
- 10.1k
- License
- BSD-3-Clause
- Last commit
- Oct 2026
- Last release
- Sep 2026
- Language
- Python
9,609 on 31 Mar 202610,071 on 20 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
sktime provides a unified fit/predict interface for forecasting, time series classification, regression, clustering, and anomaly and changepoint detection. It includes native algorithms and interfaces to statsmodels, tsfresh, PyOD, and Prophet. Pipelining, ensembling, tuning, and reduction let an algorithm built for one task be applied to another.
Who it is for: Python data scientists doing forecasting or time series classification
Strengths
- Covers forecasting, classification, regression, clustering, and detection under one API
- Composition tools: pipelines, ensembles, tuning, and reduction to scikit-learn regressors
- Interfaces to statsmodels, tsfresh, PyOD, and Prophet behind the same API
- Extension templates for custom estimators; installs via pip or conda-forge
Weaknesses
- Detection, clustering, and distances modules are maturing; alignment is experimental
- Task extras install only a curated subset of soft dependencies
- conda offers only base or all-extras, no flexible dependency sets
- README does not state GPU or memory requirements
What it needs
- Needs scikit-learn, statsmodels, tsfresh, PyOD, fbprophet
- Models: scikit-learn estimators, ThetaForecaster, TimeSeriesForestClassifier
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