Welcome to skforecast
About The Project¶
Skforecast is a Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models. It works with any estimator 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 estimator (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.
Try skforecast-ai, an AI forecasting assistant that pairs a deterministic engine, powered by skforecast, with an LLM reasoning layer.
Try Skforecast Studio — an interactive, no-code application to build time series forecasting models visually, while automatically generating production-ready Python code using skforecast.
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. Discover more in our contribution guide
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¶
In the skforecast library, a Forecaster object is a comprehensive container that provides the essential functionality and methods necessary to train a forecasting model and generate predictions for future time periods.
There are several types of forecasters, each suited to a different combination of data and modeling strategy. These include single or multiple time series, direct or recursive strategies, and statistical models (ARIMA and ETS) as well as deep learning (RNN/LSTM) and foundation models. All forecaster types share a unified API for training, prediction, and validation, and they support probabilistic forecasting.
| Forecaster | Estimator | Series | Strategy | Exog | Window features | Differentiation |
|---|---|---|---|---|---|---|
| ForecasterRecursive | scikit-learn regressor | single | recursive | ✔️ | ✔️ | ✔️ |
| ForecasterDirect | scikit-learn regressor | single | direct | ✔️ | ✔️ | ✔️ |
| ForecasterRecursiveMultiSeries | scikit-learn regressor | multiple | recursive | ✔️ | ✔️ | ✔️ |
| ForecasterDirectMultiVariate | scikit-learn regressor | multiple | direct | ✔️ | ✔️ | ✔️ |
| ForecasterFoundation | pre-trained, zero-shot | single or multiple | multi-output | ✔️ | ||
| ForecasterStats | Arima, Sarimax, Ets, Arar | single | recursive | ✔️ | ||
| ForecasterRnn | Keras model (RNN/LSTM) | single or multiple | multi-output | ✔️ | ||
| ForecasterRecursiveClassifier | scikit-learn classifier | single | recursive | ✔️ | ✔️ | |
| ForecasterEquivalentDate | Rule-based (baseline) | single | recursive |
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.
AI-assisted forecasting¶
Skforecast includes machine-readable context files so AI assistants (ChatGPT, Claude, Copilot, and others) can generate accurate code. Paste https://skforecast.org/latest/llms-full.txt into any LLM, or let your IDE pick up context automatically. Learn more in AI-assisted forecasting.
For an end-to-end workflow, try skforecast-ai, an AI forecasting assistant that pairs a deterministic engine, powered by skforecast, with an LLM reasoning layer. The source code is available on GitHub.
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 About section to meet the people behind 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. (2026). skforecast (v0.24.0). Zenodo. https://doi.org/10.5281/zenodo.8382787
APA:
Amat Rodrigo, J., & Escobar Ortiz, J. (2026). skforecast (Version 0.24.0) [Computer software]. https://doi.org/10.5281/zenodo.8382787
BibTeX:
@software{skforecast,
author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
title = {skforecast},
version = {0.24.0},
month = {8},
year = {2026},
license = {BSD-3-Clause},
url = {https://skforecast.org/},
doi = {10.5281/zenodo.8382787}
}
Publications citing skforecast¶
Chamara Hewage, H., Rostami-Tabar, B., Syntetos, A., Liberatore, F., and Milano, G., “A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating Point Predictions with Probabilistic Distributions”, arXiv e-prints, Art. no. arXiv:2502.09685, 2025. doi:10.48550/arXiv.2502.09685.
Kuthe, S., Persson, C. and Glaser, B. (2025), Physics-Informed Data-Driven Prediction of Submerged Entry Nozzle Clogging with the Aid of Ab Initio Repository. steel research int. 2400800. https://doi.org/10.1002/srin.202400800
Chatzikonstantinidis, K., Afxentiou, N., Giama, E., Fokaides, P. A., & Papadopoulos, A. M. (2025). Energy management of smart buildings during crises and digital twins as an optimisation tool for sustainable urban environment. International Journal of Sustainable Energy, 44(1). https://doi.org/10.1080/14786451.2025.2455134
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, Martín-Chivelet N, Alonso-Abella M, Sanz-Saiz C, Cuenca J, de la Cruz M. Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods. Energies. 2023; 16(3):1495. https://doi.org/10.3390/en16031495
Popławski T, Dudzik S, Szeląg 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., Doumèche, 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, Iván (2023). Predición de llamadas realizadas a un Call Center. Proyecto Fin de Carrera / Trabajo Fin de Grado, E.T.S.I. de Sistemas Informáticos (UPM), Madrid.
Lopez Vega, A., & Villanueva Vargas, R. A. (2022). Sistema para la automatización de procesos hospitalarios de control para pacientes para COVID-19 usando machine learning para el Centro de Salud San Fernando.
García Álvarez, J. D. (2022). Modelo predictivo de rentabilidad de criptomonedas para un futuro cercano.
Chilet Vera, Á. (2023). Elaboración de un algoritmo predictivo para la reposición de hipoclorito en los depósitos mediante técnicas de Machine Learning (Doctoral dissertation, Universitat Politècnica de València).
Bustinza Barrial, A. A., Bautista Abanto, A. M., Alva Alfaro, D. A., Villena Sotomayor, G. M., & Trujillo Sabrera, J. M. (2022). Predicción de los valores de la demanda máxima de energía eléctrica empleando técnicas de machine learning para la empresa Nexa Resources–Cajamarquilla.
Morgado, K. Desarrollo de una técnica de gestión de activos para transformadores de distribución 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 us continue developing, maintaining, and improving this project. Every contribution, no matter the size, makes a difference. Thank you for your support!
License¶
Skforecast software: BSD-3-Clause License
Skforecast documentation: CC BY-NC-SA 4.0
Trademark: The trademark skforecast is registered with the European Union Intellectual Property Office (EUIPO) under the application number 019109684. Unauthorized use of this trademark, its logo, or any associated visual identity elements is strictly prohibited without the express consent of the owner.