skforecast 0.9.1

Forecasting time series with scikit-learn regressors. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM, XGBoost, Ranger...).

About The Project

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 (LightGBM, XGBoost, CatBoost, ...).

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. In addition, skforecast is developed according to the following priorities:

• Fast and robust prototyping. :zap:
• Validation and backtesting methods to have a realistic assessment of model performance. :mag:
• Models must be deployed in production. :hammer:
• Models must be interpretable. :crystal_ball:

Share Your Thoughts with Us

Thank you for choosing skforecast! We value your suggestions, bug reports and recommendations as they help us identify areas for improvement and ensure that skforecast meets the needs of the community. Please consider sharing your experiences, reporting bugs, making suggestions or even contributing to the codebase on GitHub. Together, let's make time series forecasting more accessible and accurate for everyone.

Documentation

For detailed information on how to use and leverage the full potential of skforecast please refer to the comprehensive documentation available at:

https://skforecast.org :books:

Installation

The default installation of skforecast only installs hard dependencies.

pip install skforecast

Specific version:

pip install skforecast==0.9.1

Latest (unstable):

pip install git+https://github.com/JoaquinAmatRodrigo/skforecast#master

Install the full version (all dependencies):

pip install skforecast[full]

Install optional dependencies:

pip install skforecast[sarimax]

pip install skforecast[plotting]

Dependencies

• Python >= 3.8

Hard dependencies

• numpy>=1.20, <1.26
• pandas>=1.2, <2.1
• tqdm>=4.57.0, <4.66
• scikit-learn>=1.0, <1.4
• optuna>=2.10.0, <3.3
• joblib>=1.1.0, <1.4

Optional dependencies

• matplotlib>=3.3, <3.8
• seaborn>=0.11, <0.13
• statsmodels>=0.12, <0.15
• pmdarima>=2.0, <2.1

What is new in skforecast 0.9.1?

Visit the release notes to view all notable changes.

• ForecasterAutoregDirect and ForecasterAutoregMultiVariate include the n_jobs argument in their fit method, allowing multi-process parallelization for improved performance.
• All backtesting and grid search functions have been extended to include the n_jobs argument, allowing multi-process parallelization for improved performance.
• Argument refit now can be also an integer in all backtesting dependent functions in modules model_selection, model_selection_multiseries, and model_selection_sarimax. This allows the Forecaster to be trained every this number of iterations.
• ForecasterAutoregMultiSeries and ForecasterAutoregMultiSeriesCustom can be trained using series of different lengths. This means that the model can handle datasets with different numbers of data points in each series.
• Bug fixes and performance improvements.

Forecasters

A Forecaster object in the skforecast library is a comprehensive container that provides essential functionality and methods for training a forecasting model and generating predictions for future points in time.

The skforecast library offers a variety of forecaster types, each tailored to specific requirements such as single or multiple time series, direct or recursive strategies, or custom predictors. Regardless of the specific forecaster type, all instances share the same API.

Forecaster	Single series	Multiple series	Recursive strategy	Direct strategy	Probabilistic prediction	Exogenous features	Custom features
ForecasterAutoreg	:heavy_check_mark:		:heavy_check_mark:		:heavy_check_mark:	:heavy_check_mark:
ForecasterAutoregCustom	:heavy_check_mark:		:heavy_check_mark:		:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
ForecasterAutoregDirect	:heavy_check_mark:			:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
ForecasterMultiSeries		:heavy_check_mark:	:heavy_check_mark:		:heavy_check_mark:	:heavy_check_mark:
ForecasterMultiSeriesCustom		:heavy_check_mark:	:heavy_check_mark:		:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
ForecasterMultiVariate		:heavy_check_mark:		:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
ForecasterSarimax	:heavy_check_mark:		:heavy_check_mark:		:heavy_check_mark:	:heavy_check_mark:

Main User Guides

• Introduction to time series and forecasting

• Recursive multi-step forecasting

• Direct multi-step forecasting

• Independent multi-series forecasting

• Dependent multi-series forecasting (Multivariate forecasting)

• Backtesting (validation) of forecasting models

• Hyperparameter tuning and lags selection of forecasting models

• Probabilistic forecasting

• Using forecasters in production

Examples and tutorials

English

• Skforecast: time series forecasting with Python and Scikit-learn

• Forecasting electricity demand with Python

• Forecasting web traffic with machine learning and Python

• Forecasting with gradient boosting: skforecast, XGBoost, LightGBM and CatBoost

• Bitcoin price prediction with Python

• Prediction intervals in forecasting models

• Multi-series forecasting

• Reducing the influence of Covid-19 on time series forecasting models

• Forecasting time series with missing values

• Intermittent demand forecasting

Español

• Skforecast: forecasting series temporales con Python y Scikit-learn

• Forecasting de la demanda eléctrica

• Forecasting de las visitas a una página web

• Forecasting con gradient boosting: skforecast, XGBoost, LightGBM y CatBoost

• Predicción del precio de Bitcoin con Python

• Workshop predicción de series temporales con machine learning Universidad de Deusto / Deustuko Unibertsitatea

• Intervalos de predicción en modelos de forecasting

• Multi-series forecasting

• Predicción de demanda intermitente

How to contribute

Primarily, skforecast development consists of adding and creating new Forecasters, new validation strategies, or improving the performance of the current code. However, there are many other ways to contribute:

• Submit a bug report or feature request on GitHub Issues.
• Contribute a Jupyter notebook to our examples.
• Write unit or integration tests for our project.
• Answer questions on our issues, Stack Overflow, and elsewhere.
• Translate our documentation into another language.
• Write a blog post, tweet, or share our project with others.

For more information on how to contribute to skforecast, see our Contribution Guide.

Citation

If you use this software, please cite it using the following metadata.

APA:

Amat Rodrigo, J., & Escobar Ortiz, J. skforecast (Version 0.9.1) [Computer software]

BibTeX:

@software{skforecast,
author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
license = {BSD 3-Clause License},
month = {7},
title = {{skforecast}},
version = {0.9.1},
year = {2023}
}

View the citation file.

Donating

If you found skforecast useful, you can support us with a donation. Your contribution will help to continue developing and improving this project. Many thanks!

License

BSD 3-Clause License