{"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"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Skforecast is a Python library for time series forecasting that works with any scikit-learn compatible estimator, providing tools for feature engineering, model selection, hyperparameter tuning, and backtesting.","skillfed_tags":["time-series-forecasting","scikit-learn-compatible","machine-learning"],"use_cases":["Build multi-step ahead forecasts for time series data using scikit-learn compatible regressors.","Perform hyperparameter tuning and model selection for forecasting tasks using optuna integration.","Backtest forecasting models on historical data to evaluate realistic performance before deployment.","Forecast multiple related time series simultaneously with flexible single and multi-series workflows.","Engineer lagged features and other time series transformations automatically for supervised learning.","Integrate forecasting into production pipelines using scikit-learn compatible estimators."],"what_it_does":"Skforecast bridges scikit-learn and time series forecasting by letting you use any scikit-learn compatible regressor to forecast future values. It handles the complexity of converting time series data into supervised learning problems through lagged features and provides both single-series and multi-series workflows. The library includes tools for feature engineering, model selection, hyperparameter tuning via optuna, and backtesting to validate forecasts on historical data before deployment.\n\nThe package is built on numpy, pandas, and scikit-learn, with optional acceleration via numba and parallel job execution through joblib. It targets developers and data scientists who want production-ready forecasting without building custom pipelines, and it integrates naturally into existing scikit-learn workflows. The library is actively maintained, supports Python 3.10 through 3.14, and is NumFOCUS affiliated.","worth_installing":"Yes. Skforecast is actively maintained, has low install friction, carries a permissive BSD-3-Clause license, and has no known vulnerabilities. It solves a genuine problem\u2014using scikit-learn models for time series forecasting\u2014with a mature API and strong community backing. Install it if you need to forecast time series with scikit-learn compatible estimators."},"id":"skforecast","links":{"html":"https://skillfed.io/packages/skforecast","md":"https://skillfed.io/packages/skforecast.md","pypi":"https://pypi.org/project/skforecast/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-08","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"skforecast","python_support":"supports_current","summary":"Skforecast is a Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models. It works with any estimator compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others."},"popularity":{"monthly_downloads":126956,"position":11757,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.23.0"}