{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"},{"label":"Application Frameworks","url":"https://skillfed.io/packages/category/software-development-libraries-application-frameworks/5"}],"enrichment":{"capability":"TSDB downloads and loads 173 open-source time-series datasets with minimal setup, handling caching and basic data cleaning automatically so you can focus on analysis.","skillfed_tags":["time-series-data","dataset-management","benchmarking"],"use_cases":["Benchmark time-series forecasting models against standard datasets like PhysioNet or PeMS traffic data without manual downloading.","Rapidly prototype imputation and classification algorithms on diverse datasets (air quality, electricity, vessel AIS) with one-line loading.","Access all 163 UCR & UEA classification datasets for time-series classification research without managing individual downloads.","Manage dataset caching and storage on resource-constrained machines by migrating the cache directory to external disks.","Build reproducible research pipelines where dataset versions and availability are centrally maintained by the TSDB project."],"what_it_does":"TSDB is a dataset management library that centralizes access to 173 public time-series datasets commonly used in research and benchmarking. It handles downloading, extraction, and caching automatically, letting you load datasets with a single function call. The library is designed to decouple dataset management from machine learning algorithms, so you can experiment with different datasets without writing custom download and preprocessing code.\n\nThe package performs minimal data processing\u2014mainly removing invalid samples\u2014and provides both processed and raw dataset access. It manages a local cache (by default in ~/.pypots/tsdb) and lets you list available datasets, delete specific cached items, or migrate the cache directory to external storage. Dependencies are standard data-science libraries (numpy, scipy, pandas, scikit-learn, requests, tqdm), making it straightforward to integrate into existing workflows.","worth_installing":"Yes. TSDB is actively maintained, has no known vulnerabilities, low install friction, and solves a real friction point\u2014downloading and organizing public time-series datasets. It is well-suited for researchers and engineers working on time-series tasks who want to avoid manual dataset management. The permissive BSD license poses no barrier. Install it if you regularly work with benchmark time-series data."},"id":"tsdb","links":{"html":"https://skillfed.io/packages/tsdb","md":"https://skillfed.io/packages/tsdb.md","pypi":"https://pypi.org/project/tsdb/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-09","license_spdx":null,"license_treatment":"permissive","name":"tsdb","python_support":"supports_current","summary":"TSDB (Time Series Data Beans): a Python toolbox helping load 172 open-source time-series datasets"},"popularity":{"monthly_downloads":127808,"position":11730,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8"}