{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/15"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"Pyts provides time series classification algorithms and preprocessing tools, including transformations like symbolic approximation, bag-of-words encoding, and image-based representations, along with specialized classifiers and metrics for temporal data.","skillfed_tags":["time-series","classification","feature-extraction"],"use_cases":["Classify sensor time series (e.g., accelerometer data, ECG signals) using shapelet or bag-of-words approaches.","Transform raw time series into symbolic or image representations for use with standard classifiers.","Compute specialized distance metrics (Dynamic Time Warping) between temporal sequences for nearest-neighbor classification.","Preprocess multivariate time series (e.g., stock prices, weather data) sample-wise before feeding to downstream models.","Benchmark your own time series classifier against state-of-the-art algorithms using UCR/UEA datasets."],"what_it_does":"Pyts is a scikit-learn-style library for time series classification that implements state-of-the-art algorithms and provides a suite of preprocessing and transformation tools. It bridges the gap between raw temporal data and machine learning by offering multiple ways to represent time series\u2014from symbolic approximations (Piecewise Aggregate Approximation, Symbolic Aggregate approXimation) to image-based encodings (Recurrence Plot, Gramian Angular Field) to bag-of-words models. The library also includes specialized classifiers like SAXVSM, BOSSVS, Learning Shapelets, and Time Series Forest, as well as time-series-specific metrics such as Dynamic Time Warping variants and the BOSS metric.\n\nThe package is built on top of numpy, scipy, scikit-learn, joblib, and numba, inheriting their maturity and performance characteristics. It is designed for developers and researchers who need to classify temporal sequences\u2014whether univariate or multivariate\u2014without building these algorithms from scratch. The modular structure (approximation, bag_of_words, classification, decomposition, image, metrics, preprocessing, transformation) allows you to compose pipelines for custom workflows, and it includes utilities to fetch benchmark datasets from the UEA & UCR Time Series Classification Repository.","worth_installing":"Yes, if you need time series classification and want a well-structured, scikit-learn-compatible library with multiple algorithms and transformations. The permissive license, low install friction, and established dependencies make it low-risk. However, the last release was in June 2023, so verify that the package's algorithms and dependencies align with your Python and library versions before committing to production use."},"id":"pyts","links":{"html":"https://skillfed.io/packages/pyts","md":"https://skillfed.io/packages/pyts.md","pypi":"https://pypi.org/project/pyts/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2023-06-18","license_spdx":null,"license_treatment":"permissive","name":"pyts","python_support":"unspecified","summary":"A python package for time series classification"},"popularity":{"monthly_downloads":131673,"position":11579,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.13.0"}