{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"mlforecast trains machine learning models on time series data and generates forecasts, with built-in support for feature engineering, multiple models, and distributed training across remote clusters.","skillfed_tags":["time-series-forecasting","distributed-training","feature-engineering"],"use_cases":["Train models across multiple time series and generate multi-step forecasts with automatic lag feature updates.","Optimize hyperparameters across multiple models using cross-validation splits cached during training.","Forecast demand by combining lag features, rolling statistics, and exogenous variables.","Distribute training across a Dask or Ray cluster to handle large numbers of time series.","Generate prediction intervals using Conformal Prediction to quantify forecast uncertainty.","Pretrain a model on one set of series and fine-tune on a different series with limited history."],"what_it_does":"mlforecast is a time series forecasting framework that trains regressors on multiple series simultaneously. It automates feature engineering\u2014lag creation, rolling statistics, date features\u2014and handles the recursive prediction strategy needed to forecast multiple steps ahead. The core workflow is: load data in long format (one row per series-timestamp pair), define models and features, call fit() to train, then predict(n) to generate n-step forecasts.\n\nThe package is designed for production use and scales to large datasets through optional integration with Dask, Ray, or Spark clusters. It supports exogenous variables, static covariates, probabilistic forecasting via Conformal Prediction, and cross-validation for model evaluation. Dependencies include pandas for data handling, coreforecast for optimized feature computation, narwhals for dataframe abstraction, and optuna for hyperparameter optimization.","worth_installing":"Yes. mlforecast is actively maintained, has no known vulnerabilities, and provides a production-ready framework for time series forecasting at scale. It combines efficient feature engineering with optional distributed training. Install if you need to forecast multiple time series with machine learning models; skip if you need deep learning or univariate statistical methods only."},"id":"mlforecast","links":{"html":"https://skillfed.io/packages/mlforecast","md":"https://skillfed.io/packages/mlforecast.md","pypi":"https://pypi.org/project/mlforecast/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-10","license_spdx":null,"license_treatment":"permissive","name":"mlforecast","python_support":"supports_current","summary":"Scalable machine learning based time series forecasting"},"popularity":{"monthly_downloads":494140,"position":6352,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.0"}