Welcome to skforecast

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.
• Validation and backtesting methods to have a realistic assessment of model performance.
• Models must be deployed in production.
• Models must be interpretable.

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.

Installation

The default installation of skforecast only installs hard dependencies.

pip install skforecast

Specific version:

pip install skforecast==0.8.1

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, <1.25
• pandas>=1.2, <2.1
• tqdm>=4.57.0, <4.65
• scikit-learn>=1.0, <1.3
• optuna>=2.10.0, <3.2
• joblib>=1.1.0, <1.3.0

Optional dependencies

• matplotlib>=3.3, <3.8
• seaborn>=0.11, <0.13
• statsmodels>=0.12, <0.14
• pmdarima>=2.0, <2.1

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	✔️		✔️		✔️	✔️
ForecasterAutoregCustom	✔️		✔️		✔️	✔️	✔️
ForecasterAutoregDirect	✔️			✔️	✔️	✔️
ForecasterMultiSeries		✔️	✔️		✔️	✔️
ForecasterMultiSeriesCustom		✔️	✔️		✔️	✔️	✔️
ForecasterMultiVariate		✔️		✔️	✔️	✔️
ForecasterSarimax	✔️		✔️		✔️	✔️

Features

• Create Forecasters from any regressor that follows the scikit-learn API
• Include exogenous variables as predictors
• Include custom predictors (rolling mean, rolling variance ...)
• Multiple backtesting methods for model validation
• Grid search, random search and Bayesian search to find optimal lags (predictors) and best hyperparameters
• Prediction interval estimated by bootstrapping and quantile regression
• Include custom metrics for model validation and grid search
• Get predictor importance
• Forecaster 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: 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: 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 demanda intermitente

How to contribute

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

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!

Citation

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

APA:

Amat Rodrigo, J., & Escobar Ortiz, J. skforecast (Version 0.8.1) [Computer software]

BibTeX:

@software{skforecast,
author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
license = {BSD 3-Clause License},
month = {5},
title = {{skforecast}},
version = {0.8.1},
year = {2023}
}

View the citation file.

License

BSD 3-Clause License