Welcome to skforecast
About The Project¶
Skforecast is a Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.
Why use skforecast?¶
Skforecast simplifies time series forecasting with machine learning by providing:
Seamless integration with any scikit-learn compatible regressor (e.g., LightGBM, XGBoost, CatBoost, etc.).
Flexible workflows that allow for both single and multi-series forecasting.
Comprehensive tools for feature engineering, model selection, hyperparameter tuning, and more.
Production-ready models with interpretability and validation methods for backtesting and realistic performance evaluation.
Whether you're building quick prototypes or deploying models in production, skforecast ensures a fast, reliable, and scalable experience.
Get Involved¶
We value your input! Here are a few ways you can participate:
- Report bugs and suggest new features on our GitHub Issues page.
- Contribute to the project by submitting code, adding new features, or improving the documentation.
- Share your feedback on LinkedIn to help spread the word about skforecast!
Together, we can make time series forecasting accessible to everyone.
Installation & Dependencies¶
To install the basic version of skforecast with core dependencies, run the following:
pip install skforecast
For more installation options, including dependencies and additional features, check out our Installation Guide.
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 | Time series differentiation | Exogenous features | Window features |
|---|---|---|---|---|---|---|---|---|
| ForecasterRecursive | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | ||
| ForecasterDirect | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | |||
| ForecasterRecursiveMultiSeries | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | ||
| ForecasterDirectMultiVariate | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | |||
| ForecasterRNN | 鉁旓笍 | 鉁旓笍 | ||||||
| ForecasterSarimax | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 | 鉁旓笍 |
Features¶
Skforecast provides a set of key features designed to make time series forecasting with machine learning easy and efficient. For a detailed overview, see the User Guides.
Examples and tutorials¶
Explore our extensive list of examples and tutorials (English and Spanish) to get you started with skforecast. You can find them here.
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.
Visit our authors section to meet all the contributors to skforecast.
Citation¶
If you use skforecast for a scientific publication, we would appreciate citations to the published software.
Zenodo
Amat Rodrigo, Joaquin, & Escobar Ortiz, Javier. (2024). skforecast (v0.14.0). Zenodo. https://doi.org/10.5281/zenodo.8382788
APA:
Amat Rodrigo, J., & Escobar Ortiz, J. (2024). skforecast (Version 0.14.0) [Computer software]. https://doi.org/10.5281/zenodo.8382788
BibTeX:
@software{skforecast,
author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
title = {skforecast},
version = {0.14.0},
month = {11},
year = {2024},
license = {BSD-3-Clause},
url = {https://skforecast.org/},
doi = {10.5281/zenodo.8382788}
}
Publications citing skforecast¶
Sanan, O., Sperling, J., Greene, D., & Greer, R. (2024, April). Forecasting Weather and Energy Demand for Optimization of Renewable Energy and Energy Storage Systems for Water Desalination. In 2024 IEEE Conference on Technologies for Sustainability (SusTech) (pp. 175-182). IEEE. https://doi.org/10.1109/SusTech60925.2024.10553570
Bojer, A. K., Biru, B. H., Al-Quraishi, A. M. F., Debelee, T. G., Negera, W. G., Woldesillasie, F. F., & Esubalew, S. Z. (2024). Machine learning and remote sensing based time series analysis for drought risk prediction in Borena Zone, Southwest Ethiopia. Journal of Arid Environments, 222, 105160. https://doi.org/10.1016/j.jaridenv.2024.105160
V. Negri, A. Mingotti, R. Tinarelli and L. Peretto, "Comparison Between the Machine Learning and the Statistical Approach to the Forecasting of Voltage, Current, and Frequency," 2023 IEEE 13th International Workshop on Applied Measurements for Power Systems (AMPS), Bern, Switzerland, 2023, pp. 01-06, doi: 10.1109/AMPS59207.2023.10297192. https://doi.org/10.1109/AMPS59207.2023.10297192
Marcillo Vera, F., Rosado, R., Zambrano, P., Velastegui, J., Morales, G., Lagla, L., & Herrera, A. (2024). Forecasting con Python, caso de estudio: visitas a las redes sociales en Ecuador con machine learning. CONECTIVIDAD, 5(2), 15-29.
OUKHOUYA, H., KADIRI, H., EL HIMDI, K., & GUERBAZ, R. (2023). Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models. Statistics, Optimization & Information Computing, 12(1), 200-209. https://doi.org/10.19139/soic-2310-5070-1822
DUDZIK, S., & Kowalczyk, B. (2023). Prognozowanie produkcji energii fotowoltaicznej z wykorzystaniem platformy NEXO i VRM Portal. Przeglad Elektrotechniczny, 2023(11). doi:10.15199/48.2023.11.41
Polo J, Mart铆n-Chivelet N, Alonso-Abella M, Sanz-Saiz C, Cuenca J, de la Cruz M. Exploring the PV Power Forecasting at Building Fa莽ades Using Gradient Boosting Methods. Energies. 2023; 16(3):1495. https://doi.org/10.3390/en16031495
Pop艂awski T, Dudzik S, Szel膮g P. Forecasting of Energy Balance in Prosumer Micro-Installations Using Machine Learning Models. Energies. 2023; 16(18):6726. https://doi.org/10.3390/en16186726
Harrou F, Sun Y, Taghezouit B, Dairi A. Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting. Energies. 2023; 16(18):6731. https://doi.org/10.3390/en16186731
Amara-Ouali, Y., Goude, Y., Doum猫che, N., Veyret, P., Thomas, A., Hebenstreit, D., ... & Phe-Neau, T. (2023). Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge. arXiv preprint arXiv:2306.06142.
Emami, P., Sahu, A., & Graf, P. (2023). BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting. arXiv preprint arXiv:2307.00142.
Dang, HA., Dao, VD. (2023). Building Power Demand Forecasting Using Machine Learning: Application for an Office Building in Danang. In: Nguyen, D.C., Vu, N.P., Long, B.T., Puta, H., Sattler, KU. (eds) Advances in Engineering Research and Application. ICERA 2022. Lecture Notes in Networks and Systems, vol 602. Springer, Cham. https://doi.org/10.1007/978-3-031-22200-9_32
Morate del Moral, Iv谩n (2023). Predici贸n de llamadas realizadas a un Call Center. Proyecto Fin de Carrera / Trabajo Fin de Grado, E.T.S.I. de Sistemas Inform谩ticos (UPM), Madrid.
Lopez Vega, A., & Villanueva Vargas, R. A. (2022). Sistema para la automatizaci贸n de procesos hospitalarios de control para pacientes para COVID-19 usando machine learning para el Centro de Salud San Fernando.
Garc铆a 脕lvarez, J. D. (2022). Modelo predictivo de rentabilidad de criptomonedas para un futuro cercano.
Chilet Vera, 脕. (2023). Elaboraci贸n de un algoritmo predictivo para la reposici贸n de hipoclorito en los dep贸sitos mediante t茅cnicas de Machine Learning (Doctoral dissertation, Universitat Polit猫cnica de Val猫ncia).
Bustinza Barrial, A. A., Bautista Abanto, A. M., Alva Alfaro, D. A., Villena Sotomayor, G. M., & Trujillo Sabrera, J. M. (2022). Predicci贸n de los valores de la demanda m谩xima de energ铆a el茅ctrica empleando t茅cnicas de machine learning para la empresa Nexa Resources鈥揅ajamarquilla.
Morgado, K. Desarrollo de una t茅cnica de gesti贸n de activos para transformadores de distribuci贸n basada en sistema de monitoreo (Doctoral dissertation, Universidad Nacional de Colombia).
Zafeiriou A., Chantzis G., Jonkaitis T., Fokaides P., Papadopoulos A., 2023, Smart Energy Strategy - A Comparative Study of Energy Consumption Forecasting Machine Learning Models, Chemical Engineering Transactions, 103, 691-696.
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! 
