Hi all, I'm delighted to announce the latest release of skforecast! In this release (0.9.0), we have made significant improvements to enhance performance and deliver an even better experience. Key highlights of this release: 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: We have refactored our backtesting and fit methods to leverage multi-processing parallelization, resulting in faster and more efficient computations. 𝐄𝐱𝐩𝐚𝐧𝐝𝐞𝐝 𝐛𝐚𝐜𝐤𝐭𝐞𝐬𝐭𝐢𝐧𝐠 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲: With new backtesting configurations, you now have more control over when the forecaster is retrained. This allows for better evaluation and fine-tuning of different scenarios. 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