MLForecast is a Python framework and library for time series forecasting that applies machine learning models to large-scale data sets. It simplifies the typically complex process of automated feature engineering — including lags, rolling statistics and date-based features — and is one of the few libraries with native support for distributed computing frameworks like Spark and Dask, ensuring scalability. It also supports probabilistic forecasting using methods such as conformal prediction, providing quantitative measures of forecast uncertainty. In our evaluation, MLForecast scaled efficiently to millions of data points and consistently outperformed comparable tools. For teams looking to rapidly operationalize time series forecasting on high-volume data, MLForecast is a compelling choice.
