Thread by @nixtlainc

🎉 We are thrilled to announce the release of the latest version of mlforecast a #Python library for Scalable #machinelearning 🤖 for #timeseries #forecasting

🚀 This version comes with exciting new features that are sure to make forecasting even more efficient and accurate

🧵

🔮 Conformal Prediction: We've added the ability to generate probabilistic forecasts using conformal prediction. You can choose the levels of your intervals and the number of conformity scores to use.

⚡️ In addition, mlforecast includes the capability to fit multiple time series with global models. That means you don’t need to calculate sequential conformal intervals for each series and period. I.e., conformal prediction for time series just got a lot faster and easier. 🤯

🤖 Models: We've expanded our model selection to include some of your favorite sklearn methods, including LinearRegression, KNeighborsRegressor, MLPRegressor, and tree-based models like LightGBM and XGBoost.

📊 Static and Exogenous Variables: Our models can now take static and exogenous variables, giving you even more flexibility in your forecasting tasks.

🧠 Training Improvements: You can now train one model per horizon or generate recursive forecasts for the whole horizon.

📚 New Tutorials and Documentation: We've added resources to help you master probabilistic forecasting (conformal prediction), cross-validation, transfer learning, and detecting demand peaks.

Thank you for your support, and happy forecasting! 🌟
⭐️ Repo: github.com/nixtla/mlforec…