{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"Downloads and loads curated time-series forecasting datasets (Favorita, M3, M4, M5, Hierarchical, Longhorizon, PHM2008) into pandas DataFrames, fetching from remote storage on first use.","skillfed_tags":["time-series","benchmark-datasets","forecasting"],"use_cases":["Benchmark a new forecasting model against standard datasets like M4 or M5 without manual download and preprocessing.","Build hierarchical forecasting pipelines using the Hierarchical dataset to test reconciliation methods.","Evaluate retail demand forecasting on Favorita data for supply-chain optimization projects.","Compare long-horizon forecasting approaches using the Longhorizon dataset.","Prototype prognostics and health management models with PHM2008 industrial sensor data."],"what_it_does":"datasetsforecast is a data-loading library that provides programmatic access to standard time-series forecasting benchmarks. It wraps several well-known datasets (Favorita, M3, M4, M5, Hierarchical, Longhorizon, PHM2008) and handles remote fetching and local caching, so you can load them into pandas DataFrames with a single function call. Each dataset module exposes a `load()` method that accepts a local directory and a group identifier, returning train and test splits ready for model evaluation.\n\nThe package is designed for researchers and practitioners building and benchmarking forecasting models. It depends on numpy, pandas, scikit-learn, requests, aiohttp, tqdm, xlrd, and utilsforecast\u2014all widely used libraries. The project is actively maintained, supports Python 3.10 through 3.14, and carries a permissive MIT license. It is marked Pre-Alpha, meaning the API may change, but it has been in use since 2022 and receives regular updates.","worth_installing":"Yes, if you are building or benchmarking time-series forecasting models. The package eliminates manual dataset acquisition and provides standardized splits for reproducible research. Low install friction, active maintenance, and no known vulnerabilities make it a safe choice. Pre-Alpha status is not a blocker for research use, but be aware the API may change in future releases."},"id":"datasetsforecast","links":{"html":"https://skillfed.io/packages/datasetsforecast","md":"https://skillfed.io/packages/datasetsforecast.md","pypi":"https://pypi.org/project/datasetsforecast/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-24","license_spdx":null,"license_treatment":"permissive","name":"datasetsforecast","python_support":"supports_current","summary":"Datasets for Time series forecasting"},"popularity":{"monthly_downloads":99701,"position":13014,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.1"}