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...).
Documentation: https://joaquinamatrodrigo.github.io/skforecast/
Installation
pip install skforecast
Specific version:
pip install skforecast==0.5.1
Latest (unstable):
pip install git+https://github.com/JoaquinAmatRodrigo/skforecast#master
Dependencies
- numpy>=1.20, <=1.23
- pandas>=1.2, <=1.4
- tqdm>=4.57.0, <=4.64
- scikit-learn>=1.0, <=1.1.2
- statsmodels>=0.12, <=0.13.2
- matplotlib>=3.3, <=3.5
- seaborn==0.11.2
- optuna==2.10.0
- scikit-optimize==0.9.0
- joblib>=1.1.0, <=1.2.0
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-series 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
What is coming in the new release?
- Define individual time-based weights for the series,
ForecasterAutoreg
,ForecasterAutoregCustom
,ForecasterAutoregDirect
. - Define individual weights for the series,
ForecasterAutoregMultiSeries
. - Predict and Backtest all series at the same time with
ForecasterAutoregMultiSeries
. - Multiple metrics in
grid_search_forecaster_multiseries
andrandom_search_forecaster_multiseries
,ForecasterAutoregMultiSeries
. - Modeling multivariate time series,
ForecasterAutoregMultivariate
. - Bug fixes and performance improvements.
Try it:
pip install git+https://github.com/JoaquinAmatRodrigo/skforecast@0.6.x
Visit the changelog to view all notable changes.
Documentation
The documentation for the latest release is at skforecast docs.
Recent improvements are highlighted in the release notes.
Examples and tutorials
English
-
Skforecast: time series forecasting with Python and Scikit-learn
-
Forecasting time series with gradient boosting: Skforecast, XGBoost, LightGBM and CatBoost
Español
-
Skforecast: forecasting series temporales con Python y Scikit-learn
-
Forecasting series temporales con gradient boosting: Skforecast, XGBoost, LightGBM y CatBoost
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 0.5.1) [Computer software]
BibTeX:
@software{skforecast,
author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
license = {MIT},
month = {10},
title = {{skforecast}},
version = {0.5.1},
year = {2022}
}
View the citation file.
License
joaquinAmatRodrigo/skforecast is licensed under the MIT License, a short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications and larger works may be distributed under different terms and without source code.