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

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
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