Sktime

Welcome to sktime

<a href="https://www.sktime.net"><img src="https://github.com/sktime/sktime/blob/main/docs/source/images/sktime-logo.svg" width="175" align="right" /></a>

A unified interface for machine learning with time series

:rocket: Version 0.40.1 out now! Check out the release notes here.

sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes forecasting, time series classification, clustering, anomaly/changepoint detection, and other tasks. It comes with time series algorithms and scikit-learn compatible tools to build, tune, and validate time series models.

| | Documentation · Tutorials · Release Notes | |---|---| | Open Source | | | Tutorials | | | Community | | | CI/CD | | | Code | | | Downloads | | | Citation | |

:books: Documentation

| Documentation | | |--------------------------------------| -------------------------------------------------------------- | | :star: Tutorials | New to sktime? Here's everything you need to know! | | :clipboard: Binder Notebooks | Example notebooks to play with in your browser. | | :woman_technologist: Examples | How to use sktime and its features. | | :scissors: [Extension Templates] | How to build your own estimator using sktime's API. | | :control_knobs: API Reference | The detailed reference for sktime's API. | | :tv: Video Tutorial | Our video tutorial from 2021 PyData Global. | | :hammer_and_wrench: Changelog | Changes and version history. | | :deciduous_tree: Roadmap | sktime's software and community development plan. | | :pencil: Related Software | A list of related software. |

:speech_balloon: Where to ask questions

Questions and feedback are extremely welcome! We strongly believe in the value of sharing help publicly, as it allows a wider audience to benefit from it.

| Type | Platforms | | ------------------------------- | --------------------------------------- | | :bug: Bug Reports | GitHub Issue Tracker | | :sparkles: Feature Requests & Ideas | GitHub Issue Tracker | | :woman_technologist: Usage Questions | GitHub Discussions · Stack Overflow | | :speech_balloon: General Discussion | GitHub Discussions | | :factory: Contribution & Development | dev-chat channel · Discord | | :globe_with_meridians: Meet-ups and collaboration sessions | Discord - Fridays 13 UTC, dev/meet-ups channel |

:dizzy: Features

Our objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety. sktime provides a unified interface for distinct but related time series learning tasks. It features dedicated time series algorithms and tools for composite model building, such as pipelining, ensembling, tuning, and reduction, empowering users to apply algorithms designed for one task to another.

sktime also provides interfaces to related libraries, for example scikit-learn, statsmodels, tsfresh, PyOD, and fbprophet, among others.

| Module | Status | Links | |---|---|---| | Forecasting | stable | Tutorial · API Reference · Extension Template | | Time Series Classification | stable | Tutorial · API Reference · Extension Template | | Time Series Regression | stable | API Reference | | [Transformations] | stable | Tutorial · API Reference · Extension Template | | Detection tasks | maturing | Extension Template | | [Parameter fitting] | maturing | API Reference · Extension Template | | Time Series Clustering | maturing | API Reference · Extension Template | | Time Series Distances/Kernels | maturing | Tutorial · API Reference · Extension Template | | [Time Series Alignment] | experimental | API Reference · Extension Template | | [Time Series Splitters] | maturing | Extension Template | | | [Distributions and simulation] | experimental | |