{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/6"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"tslearn provides machine learning algorithms optimized for time series data, including clustering, classification, regression, and distance metrics like Dynamic Time Warping, built on scikit-learn's API.","skillfed_tags":["time-series","scikit-learn-compatible","dtw"],"use_cases":["Classify time series sequences using k-nearest neighbors with time-aware distance metrics","Cluster temporal data with TimeSeriesKMeans to find similar patterns across time series","Measure similarity between time series using Dynamic Time Warping or other specialized metrics","Preprocess variable-length time series with scaling and resampling before model training","Build scikit-learn pipelines combining time series preprocessing and classification steps"],"what_it_does":"tslearn is a machine learning toolkit built on top of scikit-learn, numpy, scipy, and numba that specializes in time series data. It provides clustering (TimeSeriesKMeans), classification (KNeighborsTimeSeriesClassifier), regression, and distance metrics including Dynamic Time Warping. The package expects time series as 3D numpy arrays and handles variable-length sequences.\n\nThe library integrates seamlessly with scikit-learn's ecosystem, supporting pipelines and hyperparameter tuning. It includes utilities for data preprocessing (scaling, resampling, piecewise transformations), dataset loading (UCR datasets), and analysis tasks like computing barycenters. Models follow scikit-learn's fit/predict API, making it accessible to developers already familiar with that framework.","worth_installing":"Yes. tslearn is actively maintained, has no known vulnerabilities, uses permissive licensing, and offers low installation friction. It fills a clear gap for time series machine learning with scikit-learn compatibility. Install if you need specialized time series algorithms; skip if your use case fits standard scikit-learn or you require production-grade time series forecasting."},"id":"tslearn","links":{"html":"https://skillfed.io/packages/tslearn","md":"https://skillfed.io/packages/tslearn.md","pypi":"https://pypi.org/project/tslearn/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-02","license_spdx":"BSD-2-Clause","license_treatment":"permissive","name":"tslearn","python_support":"supports_current","summary":"A machine learning toolkit dedicated to time-series data"},"popularity":{"monthly_downloads":313462,"position":7714,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.0"}