{"library":"mlforecast","type":"library","category":null,"description":"MLForecast is a framework for scalable machine learning based time series forecasting. It enables users to apply various machine learning models (like scikit-learn, LightGBM, XGBoost) to time series data, handling complex feature engineering (lags, rolling statistics, date features) and offering distributed training capabilities. The library is actively maintained, with frequent releases, currently at version 1.0.31.","language":"python","status":"active","version":"1.0.31","tags":["time series","forecasting","machine learning","scalable","distributed","feature engineering"],"install":[{"cmd":"pip install mlforecast","imports":["from mlforecast import MLForecast","import lightgbm as lgb\nmodels = [lgb.LGBMRegressor()]","from mlforecast.lag_transforms import ExpandingMean","from mlforecast.target_transforms import Differences"]},{"cmd":"pip install \"mlforecast[polars]\"","imports":[]},{"cmd":"pip install \"mlforecast[dask]\"","imports":[]},{"cmd":"pip install \"mlforecast[ray]\"","imports":[]},{"cmd":"pip install \"mlforecast[spark]\"","imports":[]}],"homepage":"https://mlforecast.ai","github":"https://github.com/Nixtla/mlforecast","docs":"https://nixtlaverse.nixtla.io/mlforecast/","changelog":null,"pypi":"https://pypi.org/project/mlforecast/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":21.4,"avg_import_s":4.36,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/mlforecast/compatibility"},"provenance":{"verified_status":"passing","verified_at":"Sun Jun 28","last_verified":"Sun Jun 28","next_check":"Tue Jul 28","install_tag":null}}