Hi all, I'm delighted to announce the latest release of *s**kforecast*! *Skforecast *is a Python library that eases using scikit-learn regressors as single and multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM, XGBoost, Ranger...). Docs: https://skforecast.org/ Why use skforecast? The fields of statistics and machine learning have developed many excellent regression algorithms that can be useful for forecasting, but applying them effectively to time series analysis can still be a challenge. To address this issue, the skforecast library provides a comprehensive set of tools for training, validation and prediction in a variety of scenarios commonly encountered when working with time series. The library is built using the widely used scikit-learn API, making it easy to integrate into existing workflows. With skforecast, users have access to a wide range of functionalities such as feature engineering, model selection, hyperparameter tuning and many others. This allows users to focus on the essential aspects of their projects and leave the intricacies of time series analysis to skforecast. Happy forecasting! -- Joaquín Amat Rodrigo