Understand the project
Practical overview
When it fits
Use Sktime when a Python project covers multiple time-series learning tasks or needs a consistent way to compare, tune and validate models across them.
Who it is for
- Python developers building time-series machine-learning workflows
- Data scientists comparing models across forecasting and other time-series tasks
- Teams working with sales, sensor, operational or transaction time series
Documented uses
- Forecast future values from historical time-series data
- Classify time-series examples such as sensor or transaction sequences
- Cluster related time series
- Detect anomalous observations or behavior
- Identify changepoints that indicate shifts in system behavior
Before you choose
Requirements & limits
The project is publicly available as open-source software under the BSD-3-Clause license. The evidence identifies Linux, Windows, macOS and CLI support, and points to Python package and Conda distribution channels.
- Python is the implementation language and the framework is designed around the scikit-learn ecosystem.
- The unified interface spans forecasting, classification, clustering, anomaly detection and changepoint detection.
- Dedicated time-series algorithms are available alongside scikit-learn-compatible tools.
- Tutorials, examples, API references, Binder notebooks and estimator extension templates are provided.
- The repository is public, active and licensed under BSD-3-Clause.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 2 October 2026
- Original website title
- sktime/sktime · Official homepage title · retained in its source language