{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/5"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"sktime provides a unified interface for time series machine learning tasks including forecasting, classification, clustering, anomaly detection, and regression, with scikit-learn compatible tools for model building and validation.","skillfed_tags":["time-series","forecasting","scikit-learn-compatible"],"use_cases":["Build and compare multiple forecasting models using a unified API without learning different library conventions","Apply time series classification algorithms to multivariate sensor or financial data with scikit-learn compatible pipelines","Detect anomalies or changepoints in streaming or batch time series data using dedicated detection algorithms","Cluster time series data using distance-based or feature-based methods with consistent model interfaces","Combine forecasting or classification with feature transformations and hyperparameter tuning in a single pipeline"],"what_it_does":"sktime is a Python library that unifies multiple time series machine learning tasks under a single, scikit-learn compatible API. It provides dedicated algorithms for forecasting, time series classification, clustering, anomaly and changepoint detection, regression, and transformations, along with tools for pipelining, ensembling, and hyperparameter tuning. The library also provides interfaces to related libraries.\n\nThe package is designed for data scientists and researchers working with temporal data who want a consistent interface across different time series problems. It supports Python 3.10 through 3.14 and has seven runtime dependencies, all standard scientific Python libraries. Active development and a large community (9920 GitHub stars) suggest ongoing maintenance and feature expansion.","worth_installing":"Yes. sktime is actively maintained, has no known vulnerabilities, low install friction, and a permissive BSD 3-Clause License. It fills a genuine gap by providing a unified interface for diverse time series tasks that would otherwise require learning multiple library APIs. Suitable for production use in forecasting, classification, and detection workflows."},"id":"sktime","links":{"html":"https://skillfed.io/packages/sktime","md":"https://skillfed.io/packages/sktime.md","pypi":"https://pypi.org/project/sktime/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-28","license_spdx":null,"license_treatment":"permissive","name":"sktime","python_support":"supports_current","summary":"A unified framework for machine learning with time series"},"popularity":{"monthly_downloads":1186166,"position":4251,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.1.0"}