From Newsgroup: comp.lang.python.announce

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:

EYEaEYEoEYEiEYEUEYEoEYELEYEREYEY EYE-EYEREYE2EYEfEYE?EYE2EYEaEYEUEYEoEYELEYER: We have refactored our backtesting
and fit methods to leverage multi-processing parallelization, resulting in faster and more efficient computations.

EYEaEYE#EYE-EYEUEYEoEYEYEYEREYEY EYEcEYEUEYELEYEnEYE!EYEREYE4EYE!EYEoEYEoEYEa EYEfEYE<EYEoEYELEYE!EYEoEYE?EYEoEYEUEYENEYEoEYE!EYE#: 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!
--
Joaqu|!n Amat Rodrigo
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