Nixtla/mlforecast

Health v2 · maintenance only

Adoption not part of the score

1269 stars · 131 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

mlforecast is a Python framework for time series forecasting using any machine learning model with fit/predict methods, providing efficient feature engineering (lags, rolling windows, target transforms) and sklearn-like syntax. It scales to millions of series via distributed backends including dask, ray, and spark, and supports probabilistic forecasting with conformal prediction.

Use cases

• forecast demand across thousands of products with machine learning
• train LightGBM or XGBoost models on many time series
• produce multi-step ahead forecasts with exogenous variables like prices
• scale time series feature engineering to large datasets with dask or spark
• generate prediction intervals for forecasts using conformal prediction
• run cross-validation for forecasting models on multiple series
• forecast sales or traffic in production with sklearn-style APIs

When to choose

• you want to use gradient boosting or other ML models for forecasting instead of statistical or neural models
• you need to forecast millions of time series and scale out with dask, ray, or spark
• you want fast, built-in lag and rolling-window feature engineering for time series
• you need exogenous variables, static covariates, and probabilistic intervals in one pipeline
• you prefer a familiar sklearn fit/predict workflow

When to avoid

• you need deep learning forecasting architectures like NBEATS or TFT (use neuralforecast instead)
• you want classical statistical methods such as ARIMA or ETS (use statsforecast instead)
• you need a pretrained foundation model for time series (consider TimeGPT)
• your data is not time series or you need general-purpose ML pipelines

Facets

library · maturity active

machine-learning data-science etl time-series machine-learning data-science analytics python cross-platform cloud forecasting time-series-forecasting feature-engineering lightgbm xgboost dask spark ray polars conformal-prediction sklearn-compatible distributed-training docker

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markdown · JSON · MCP: product_card(name="Nixtla/mlforecast")