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,
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.
Joaquín Amat Rodrigo