skforecast

Time series forecasting with machine learning models

https://github.com/skforecast/skforecast

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 6 DOI reference(s) in README
✓
Academic publication links
Links to: zenodo.org
○
Committers with academic emails
○
Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (13.4%) to scientific vocabulary

Keywords

arima autoregressive-forecasting backtesting-forecasters data-science direct-forecasting exogenous-predictors forecasting lightgbm lstm-neural-networks machine-learning multi-series-forecasting multi-step-forecasting multiple-time-series-forecasting probabilistic-forecasting python quantile-forecasting sarimax scikit-learn time-series xgboost

Last synced: 6 months ago · JSON representation ·

Repository

Time series forecasting with machine learning models

Basic Info

Host: GitHub
Owner: skforecast
License: bsd-3-clause
Language: Jupyter Notebook
Default Branch: master
Homepage: https://skforecast.org
Size: 666 MB

Statistics

Stars: 1,363
Watchers: 10
Forks: 164
Open Issues: 20
Releases: 34

Topics

Created about 5 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing Funding License Code of conduct Citation Cla

README.md

| | | | --- | --- | | Package | | | Meta | | | Testing | | |Donation | | |Community | |Affiliation |

:information_source: About The Project
:books: Documentation
:computer: Installation & Dependencies
:sparkles: What is new in skforecast 0.17?
:crystal_ball: Forecasters
:mortar_board: Examples and tutorials
:handshake: How to contribute
:memo: Citation
:moneywithwings: Donating
:scroll: License

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:

:jigsaw: Seamless integration with any scikit-learn compatible regressor (e.g., LightGBM, XGBoost, CatBoost, etc.).
:repeat: Flexible workflows that allow for both single and multi-series forecasting.
:hammerandwrench: 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.

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

| Documentation | | |:----------------------------------------|:----| | :book: Introduction to forecasting | Basics of forecasting concepts and methodologies | | :rocket: Quick start | Get started quickly with skforecast | | :hammerandwrench: User guides | Detailed guides on skforecast features and functionalities | | :mortar_board: Examples and tutorials | Learn through practical examples and tutorials to master skforecast | | :question: FAQ and tips | Find answers and tips about forecasting | | :books: API Reference | Comprehensive reference for skforecast functions and classes | | :memo: Releases | Keep track of major updates and changes | | :mag: More | Discover more about skforecast and its creators |

Installation & Dependencies

To install the basic version of skforecast with core dependencies, run the following:

bash pip install skforecast

For more installation options, including dependencies and additional features, check out our Installation Guide.

What is new in skforecast 0.17?

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

For updates to the latest stable version, see the release notes here.
For updates on the version in development (unstable), see the development release notes.

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|:heavycheckmark:||:heavycheckmark:||:heavycheckmark:|:heavycheckmark:|:heavycheckmark:|:heavycheckmark:| |ForecasterDirect|:heavycheckmark:|||:heavycheckmark:|:heavycheckmark:|:heavycheckmark:|:heavycheckmark:|:heavycheckmark:| |ForecasterRecursiveMultiSeries||:heavycheckmark:|:heavycheckmark:||:heavycheckmark:|:heavycheckmark:|:heavycheckmark:|:heavycheckmark:| |ForecasterDirectMultiVariate||:heavycheckmark:||:heavycheckmark:|:heavycheckmark:|:heavycheckmark:|:heavycheckmark:|:heavycheckmark:| |ForecasterRNN|:heavycheckmark:|:heavycheckmark:||:heavycheckmark:|:heavycheckmark:||:heavycheckmark:|| |ForecasterSarimax|:heavycheckmark:||:heavycheckmark:||:heavycheckmark:|:heavycheckmark:|:heavycheckmark:||

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 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. (2025). skforecast (v0.17.0). Zenodo. https://doi.org/10.5281/zenodo.8382788

APA: Amat Rodrigo, J., & Escobar Ortiz, J. (2025). skforecast (Version 0.17.0) [Computer software]. https://doi.org/10.5281/zenodo.8382788

BibTeX: @software{skforecast, author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier}, title = {skforecast}, version = {0.17.0}, month = {8}, year = {2025}, license = {BSD-3-Clause}, url = {https://skforecast.org/}, doi = {10.5281/zenodo.8382788} }

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.

Owner

Name: skforecast
Login: skforecast
Kind: organization
Location: Spain

Website: https://skforecast.org
Repositories: 2
Profile: https://github.com/skforecast

Repositories related to the skforecast python library

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: skforecast
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Joaquin
    family-names: Amat Rodrigo
    email: j.amatrodrigo@gmail.com
  - given-names: Javier
    family-names: Escobar Ortiz
    email: javier.escobar.ortiz@gmail.com
url: 'https://skforecast.org/'
abstract: >-
  Skforecast is a Python library that eases using
  scikit-learn regressors as single and multi-step
  forecasters. It also works with any regressor compatible
  with the scikit-learn API.
keywords:
  - forecasting
  - machine learning
  - python
doi: 10.5281/zenodo.8382788
license: bsd-3-clause
version: 0.17.0
date-released: '2025-08-11'

GitHub Events

Total

Create event: 108
Release event: 4
Issues event: 48
Watch event: 232
Delete event: 101
Member event: 1
Issue comment event: 146
Push event: 901
Pull request review comment event: 26
Pull request event: 252
Pull request review event: 121
Fork event: 34

Last Year

Create event: 108
Release event: 4
Issues event: 48
Watch event: 232
Delete event: 101
Member event: 1
Issue comment event: 146
Push event: 901
Pull request review comment event: 26
Pull request event: 252
Pull request review event: 121
Fork event: 34

Committers

Last synced: 10 months ago

All Time

Total Commits: 3,799
Total Committers: 19
Avg Commits per committer: 199.947
Development Distribution Score (DDS): 0.633

Past Year

Commits: 1,221
Committers: 11
Avg Commits per committer: 111.0
Development Distribution Score (DDS): 0.492

Top Committers

Name	Email	Commits
JavierEscobarOrtiz	j**z@g**m	1,395
Joaquín Amat Rodrigo	j**o@g**m	1,327
Joaquin Amat	j**t@v**m	967
Fernando Carazo	f**m@g**m	35
GinesMeca	g**1@h**m	19
Fernando Carazo	f**o@v**m	16
g-rubio	g**z@g**m	10
Amat Rodrigo	j**t@c**m	7
Ignacio Moya	i**d@g**m	6
Josh Wong	j**g@o**m	4
mwainwright	7****t	3
Edgar Bahilo Rodríguez	e**z@g**m	2
tyg3rr	l**3@g**m	2
Fernando da Silva	f**n@h**m	1
Ivan Liu	8****W	1
Kishan Manani	3****i	1
Pablo RP	p**r@g**m	1
Jordi Silvestre	j**e@f**m	1
Sergio Quijano	s**o@s**m	1

Committer Domains (Top 20 + Academic)

veeva.com: 2 stratesys-ts.com: 1 fmc-ag.com: 1 cepsa.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 36
Total pull requests: 282
Average time to close issues: 3 months
Average time to close pull requests: about 23 hours
Total issue authors: 28
Total pull request authors: 9
Average comments per issue: 2.67
Average comments per pull request: 0.17
Merged pull requests: 231
Bot issues: 0
Bot pull requests: 12

Past Year

Issues: 29
Pull requests: 186
Average time to close issues: about 2 months
Average time to close pull requests: 1 day
Issue authors: 24
Pull request authors: 9
Average comments per issue: 2.55
Average comments per pull request: 0.26
Merged pull requests: 144
Bot issues: 0
Bot pull requests: 12

View more stats

Top Authors

Issue Authors

JoaquinAmatRodrigo (5)
JavierEscobarOrtiz (3)
AVPokrovsky (2)
KishManani (2)
FernandoCarazoMelo (2)
clevilll (2)
hopsinson (1)
Arnechos (1)
jdalonsos (1)
Best20030421 (1)
DiptenduIDEAS (1)
valdilemke (1)
marc-heinl (1)
Khaled-Dellal (1)
GeorgeKontos14 (1)

Pull Request Authors

JoaquinAmatRodrigo (184)
JavierEscobarOrtiz (106)
dependabot[bot] (12)
GinesMeca (9)
123Soham-bhatia (6)
g-rubio (6)
FernandoCarazoMelo (3)
pablorodriper (2)
shermansiu (2)
vpekar (1)

Top Labels

Issue Labels

enhancement (6) question (5) good first issue (3) deep_learning (3) bug (2) documentation (1)

Pull Request Labels

enhancement (4) dependencies (4) deep_learning (2) github-actions (2) documentation (1)

Packages

Total packages: 2
Total downloads:
- pypi 76,456 last-month
Total docker downloads: 2,994

Total dependent packages: 8
(may contain duplicates)
Total dependent repositories: 18
(may contain duplicates)
Total versions: 55
Total maintainers: 2

pypi.org: skforecast

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.

Homepage: https://www.skforecast.org
Documentation: https://www.skforecast.org
License: bsd-3-clause
Latest release: 0.17.0
published 6 months ago

Versions: 26
Dependent Packages: 8
Dependent Repositories: 18
Downloads: 76,456 Last month
Docker Downloads: 2,994

Rankings

Dependent packages count: 1.1%

Downloads: 1.5%

Stargazers count: 2.3%

Average: 2.7%

Docker downloads count: 3.2%

Dependent repos count: 3.4%

Forks count: 4.7%

Maintainers (2)

joaquin_amat_rodrigo JavierEscobarOrtiz

Last synced: 6 months ago

proxy.golang.org: github.com/skforecast/skforecast

Documentation: https://pkg.go.dev/github.com/skforecast/skforecast#section-documentation
License: bsd-3-clause
Latest release: v0.17.0
published 6 months ago

Versions: 29
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.8%

Average: 6.0%

Dependent repos count: 6.2%

Last synced: 6 months ago

Dependencies

.github/workflows/codecov.yml actions

actions/checkout v1 composite
actions/setup-python v3 composite
codecov/codecov-action v3 composite

.github/workflows/unit-tests.yml actions

actions/checkout v3 composite
actions/setup-python v4 composite

pyproject.toml pypi

joblib >=1.1.0, <1.4
numpy >=1.20, <1.26
optuna >=2.10.0, <3.3
pandas >=1.2, <2.1
scikit-learn >=1.0, <1.4
tqdm >=4.57.0, <4.66

requirements_mkdocs.txt pypi

mike ==1.1.2
mkdocs ==1.5.3
mkdocs-jupyter ==0.24.6
mkdocs-material ==9.4.9
mkdocstrings ==0.24.0
mkdocstrings-python ==1.7.4
notebook ==6.4.12

skforecast

Science Score: 67.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Table of Contents

About The Project

Why use skforecast?

Get Involved

Documentation

Installation & Dependencies

What is new in skforecast 0.17?

Forecasters

Examples and tutorials

How to contribute

Citation

Donating

License

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: skforecast

Rankings

Maintainers (2)

proxy.golang.org: github.com/skforecast/skforecast

Rankings

Dependencies