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Table of Contents

• :information_source: About The Project
• :books: Documentation
• :computer: Installation & Dependencies
• :sparkles: What is new in skforecast?
• :crystal_ball: Forecasters
• :mortar_board: Examples and tutorials
• :handshake: How to contribute
• :memo: Citation
• :money_with_wings: Donating
• :scroll: License

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:

• :jigsaw: Seamless integration with any scikit-learn compatible estimator (e.g., LightGBM, XGBoost, CatBoost, etc.).
• :repeat: Flexible workflows that allow for both single and multi-series forecasting.
• :hammer_and_wrench: Comprehensive tools for feature engineering, model selection, hyperparameter tuning, and more.
• :building_construction: 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.

[!TIP] :sparkles: Try skforecast-ai, an AI forecasting assistant that pairs a deterministic engine, powered by skforecast, with an LLM reasoning layer.

[!TIP] :computer: Try Skforecast Studio, an interactive, no-code application to build time series forecasting models visually, while automatically generating production-ready Python code using skforecast.

Quick Example

import pandas as pd
from lightgbm import LGBMRegressor
from skforecast.recursive import ForecasterRecursive
from skforecast.datasets import load_demo_dataset

# Download demo dataset
y = load_demo_dataset()

# Create and fit the forecaster using the last 15 observations as features
forecaster = ForecasterRecursive(
                 estimator = LGBMRegressor(random_state=123, verbose=-1),
                 lags      = 15
             )
forecaster.fit(y=y)

# Predict the next 12 months
predictions = forecaster.predict(steps=12)
predictions.head()
# 2008-07-01    0.976355
# 2008-08-01    1.038296
# 2008-09-01    1.108676
# 2008-10-01    1.163778
# 2008-11-01    1.169646
# Freq: MS, Name: pred, dtype: float64

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.

Documentation

Explore the full capabilities of skforecast with our comprehensive documentation:

:books: https://skforecast.org

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.

What is new in skforecast?

All significant changes to this project are documented in the release file.

• For updates to the latest stable version, see the release notes here.

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.

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 (v0.25.0). Zenodo. https://doi.org/10.5281/zenodo.8382787

APA:

Amat Rodrigo, J., & Escobar Ortiz, J. (2026). skforecast (Version 0.25.0) [Computer software]. https://doi.org/10.5281/zenodo.8382787

BibTeX:

@software{skforecast,
  author  = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
  title   = {skforecast},
  version = {0.25.0},
  month   = {9},
  year    = {2026},
  license = {BSD-3-Clause},
  url     = {https://skforecast.org/},
  doi     = {10.5281/zenodo.8382787}
}

View the citation file.

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.