skforecast

Time series forecasting with scikit-learn regressors.

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM, XGBoost, Ranger...).

Why use skforecast?

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.

Installation

The default installation of skforecast only installs hard dependencies.

pip install skforecast

Specific version:

pip install skforecast==0.7.0

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, <1.6
• 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

Features

• Create recursive autoregressive forecasters from any regressor that follows the scikit-learn API
• Create direct autoregressive forecasters from any regressor that follows the scikit-learn API
• Create multi-time series autoregressive forecasters from any regressor that follows the scikit-learn API
• Create multivariate autoregressive 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
• Include custom metrics for model validation and grid search
• Prediction interval estimated by bootstrapping and quantile regression
• 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 time series 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

Español

Skforecast: forecasting series temporales con Python y Scikit-learn

Forecasting de la demanda eléctrica

Forecasting de las visitas a una página web

Forecasting series temporales 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

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!

How to Contribute to Skforecast

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 0.7.0) [Computer software]

BibTeX:

@software{skforecast,
author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
license = {MIT},
month = {3},
title = {{skforecast}},
version = {0.7.0},
year = {2023}
}

View the citation file.

License

joaquinAmatRodrigo/skforecast is licensed under the MIT License, a short and simple permissive license with conditions only requiring the preservation of copyright and license notices. Licensed works, modifications and larger works may be distributed under different terms and without source code.