| Name | skforecast JSON |
| Version |
0.14.0
JSON |
| download |
| home_page | None |
| Summary | 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. |
| upload_time | 2024-11-11 13:34:49 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | BSD 3-Clause License Copyright (c) 2021-2024, skforecast developers Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
data-science
machine-learning
data-mining
time-series
scikit-learn
forecasting
time-series-analysis
time-series-regression
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numpy
pandas
tqdm
scikit-learn
optuna
joblib
matplotlib
lightgbm
tensorflow
seaborn
keras
pytest-cov
pytest
tomli
pytest-xdist
numpy
statsmodels
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<h1 align="left">
<img src="https://github.com/skforecast/skforecast/blob/master/images/banner-landing-page-skforecast.png?raw=true#only-light" style= margin-top: 0px;>
</h1>
| | |
| --- | --- |
| Package |  [](https://pypi.org/project/skforecast/) [](https://pepy.tech/project/skforecast) [](https://pepy.tech/project/skforecast) [](https://github.com/skforecast/skforecast/graphs/commit-activity) [](https://www.repostatus.org/#active) |
| Meta | [](https://github.com/skforecast/skforecast/blob/master/LICENSE) [](https://zenodo.org/doi/10.5281/zenodo.8382787) |
| Testing | [](https://github.com/skforecast/skforecast/actions/workflows/unit-tests.yml/badge.svg) [](https://codecov.io/gh/skforecast/skforecast) |
|Donation | [](https://www.paypal.com/donate/?hosted_button_id=D2JZSWRLTZDL6) [](https://www.buymeacoffee.com/skforecast)  |
|Community | [](https://www.linkedin.com/company/skforecast/)
|Affiliation | [](https://numfocus.org/sponsored-projects/affiliated-projects)
# Table of Contents
- :information_source: [About The Project](#about-the-project)
- :books: [Documentation](#documentation)
- :computer: [Installation & Dependencies](#installation--dependencies)
- :sparkles: [What is new in skforecast 0.14?](#what-is-new-in-skforecast-014)
- :crystal_ball: [Forecasters](#forecasters)
- :mortar_board: [Examples and tutorials](#examples-and-tutorials)
- :handshake: [How to contribute](#how-to-contribute)
- :memo: [Citation](#citation)
- :money_with_wings: [Donating](#donating)
- :scroll: [License](#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.
- :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.
### 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](https://github.com/skforecast/skforecast/issues).
- **Contribute** to the project by [submitting code](https://github.com/skforecast/skforecast/blob/master/CONTRIBUTING.md), 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
For detailed information on how to use and leverage the full potential of **skforecast** please refer to the comprehensive documentation available at:
**https://skforecast.org** :books:
| Documentation | |
|:----------------------------------------|:----|
| :book: [Introduction to forecasting] | Basics of forecasting concepts and methodologies |
| :rocket: [Quick start] | Get started quickly with skforecast |
| :hammer_and_wrench: [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 |
| :black_nib: [Authors] | Meet the authors and contributors of skforecast |
[Introduction to forecasting]: https://skforecast.org/latest/introduction-forecasting/introduction-forecasting.html
[Quick start]: https://skforecast.org/latest/quick-start/quick-start-skforecast.html
[User guides]: https://skforecast.org/latest/user_guides/table-of-contents.html
[Examples and tutorials]: https://skforecast.org/latest/examples/examples_english.html
[FAQ and tips]: https://skforecast.org/latest/faq/table-of-contents.html
[API Reference]: https://skforecast.org/latest/api/forecasterrecursive.html
[Authors]: https://skforecast.org/latest/authors/authors.html
# 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](https://skforecast.org/latest/quick-start/how-to-install.html).
# What is new in skforecast 0.14?
All significant changes to this project are documented in the release file.
- For updates to the **latest stable version**, see the [release notes here](https://skforecast.org/latest/releases/releases.html).
- For updates on the **version in development** (unstable), see the [development release notes](https://skforecast.org/dev/releases/releases.html).
# 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]|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|
|[ForecasterDirect]|:heavy_check_mark:|||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|
|[ForecasterRecursiveMultiSeries]||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|
|[ForecasterDirectMultiVariate]||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|
|[ForecasterRNN]||:heavy_check_mark:||:heavy_check_mark:|||||
|[ForecasterSarimax]|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:||
[ForecasterRecursive]: https://skforecast.org/latest/user_guides/autoregresive-forecaster.html
[ForecasterDirect]: https://skforecast.org/latest/user_guides/direct-multi-step-forecasting.html
[ForecasterRecursiveMultiSeries]: https://skforecast.org/latest/user_guides/independent-multi-time-series-forecasting.html
[ForecasterDirectMultiVariate]: https://skforecast.org/latest/user_guides/dependent-multi-series-multivariate-forecasting.html
[ForecasterRNN]: https://skforecast.org/latest/user_guides/forecasting-with-deep-learning-rnn-lstm
[ForecasterSarimax]: https://skforecast.org/latest/user_guides/forecasting-sarimax-arima.html
# 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](https://skforecast.org/latest/examples/examples_english).
# 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](https://github.com/skforecast/skforecast/issues).
- Contribute a Jupyter notebook to our [examples](https://skforecast.org/latest/examples/examples_english).
- Write [unit or integration tests](https://docs.pytest.org/en/latest/) 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](https://github.com/skforecast/skforecast/blob/master/CONTRIBUTING.md).
Visit our [authors section](https://skforecast.org/latest/authors/authors) 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}
}
```
View the [citation file](https://github.com/skforecast/skforecast/blob/master/CITATION.cff).
# 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!
<a href="https://www.buymeacoffee.com/skforecast"><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=skforecast&button_colour=f79939&font_colour=000000&font_family=Poppins&outline_colour=000000&coffee_colour=FFDD00" /></a>
<br>
[](https://www.paypal.com/donate/?hosted_button_id=D2JZSWRLTZDL6)
# License
[BSD-3-Clause License](https://github.com/skforecast/skforecast/blob/master/LICENSE)
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It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.\n\n### Why use skforecast?\n\nSkforecast simplifies time series forecasting with machine learning by providing:\n\n- :jigsaw: **Seamless integration** with any scikit-learn compatible regressor (e.g., LightGBM, XGBoost, CatBoost, etc.).\n- :repeat: **Flexible workflows** that allow for both single and multi-series forecasting.\n- :hammer_and_wrench: **Comprehensive tools** for feature engineering, model selection, hyperparameter tuning, and more.\n- :building_construction: **Production-ready models** with interpretability and validation methods for backtesting and realistic performance evaluation.\n\nWhether you're building quick prototypes or deploying models in production, skforecast ensures a fast, reliable, and scalable experience.\n\n### Get Involved\n\nWe value your input! Here are a few ways you can participate:\n\n- **Report bugs** and suggest new features on our [GitHub Issues page](https://github.com/skforecast/skforecast/issues).\n- **Contribute** to the project by [submitting code](https://github.com/skforecast/skforecast/blob/master/CONTRIBUTING.md), adding new features, or improving the documentation.\n- **Share your feedback** on LinkedIn to help spread the word about skforecast!\n\nTogether, we can make time series forecasting accessible to everyone.\n\n\n# Documentation\n\nFor detailed information on how to use and leverage the full potential of **skforecast** please refer to the comprehensive documentation available at:\n\n**https://skforecast.org** :books:\n\n| Documentation | |\n|:----------------------------------------|:----|\n| :book: [Introduction to forecasting] | Basics of forecasting concepts and methodologies |\n| :rocket: [Quick start] | Get started quickly with skforecast |\n| :hammer_and_wrench: [User guides] | Detailed guides on skforecast features and functionalities |\n| :mortar_board: [Examples and tutorials] | Learn through practical examples and tutorials to master skforecast |\n| :question: [FAQ and tips] | Find answers and tips about forecasting |\n| :books: [API Reference] | Comprehensive reference for skforecast functions and classes |\n| :black_nib: [Authors] | Meet the authors and contributors of skforecast |\n\n[Introduction to forecasting]: https://skforecast.org/latest/introduction-forecasting/introduction-forecasting.html\n[Quick start]: https://skforecast.org/latest/quick-start/quick-start-skforecast.html\n[User guides]: https://skforecast.org/latest/user_guides/table-of-contents.html\n[Examples and tutorials]: https://skforecast.org/latest/examples/examples_english.html\n[FAQ and tips]: https://skforecast.org/latest/faq/table-of-contents.html\n[API Reference]: https://skforecast.org/latest/api/forecasterrecursive.html\n[Authors]: https://skforecast.org/latest/authors/authors.html\n\n\n# Installation & Dependencies\n\nTo install the basic version of `skforecast` with core dependencies, run the following:\n\n```bash\npip install skforecast\n```\n\nFor more installation options, including dependencies and additional features, check out our [Installation Guide](https://skforecast.org/latest/quick-start/how-to-install.html).\n\n\n# What is new in skforecast 0.14?\n\nAll significant changes to this project are documented in the release file.\n\n- For updates to the **latest stable version**, see the [release notes here](https://skforecast.org/latest/releases/releases.html).\n\n- For updates on the **version in development** (unstable), see the [development release notes](https://skforecast.org/dev/releases/releases.html).\n\n\n# Forecasters\n\nA **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.\n\nThe **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.\n\n| Forecaster | Single series | Multiple series | Recursive strategy | Direct strategy | Probabilistic prediction | Time series differentiation | Exogenous features | Window features |\n|:-----------|:-------------:|:---------------:|:------------------:|:---------------:|:------------------------:|:---------------------------:|:------------------:|:---------------:|\n|[ForecasterRecursive]|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|\n|[ForecasterDirect]|:heavy_check_mark:|||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|\n|[ForecasterRecursiveMultiSeries]||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|\n|[ForecasterDirectMultiVariate]||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|\n|[ForecasterRNN]||:heavy_check_mark:||:heavy_check_mark:|||||\n|[ForecasterSarimax]|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:||\n\n[ForecasterRecursive]: https://skforecast.org/latest/user_guides/autoregresive-forecaster.html\n[ForecasterDirect]: https://skforecast.org/latest/user_guides/direct-multi-step-forecasting.html\n[ForecasterRecursiveMultiSeries]: https://skforecast.org/latest/user_guides/independent-multi-time-series-forecasting.html\n[ForecasterDirectMultiVariate]: https://skforecast.org/latest/user_guides/dependent-multi-series-multivariate-forecasting.html\n[ForecasterRNN]: https://skforecast.org/latest/user_guides/forecasting-with-deep-learning-rnn-lstm\n[ForecasterSarimax]: https://skforecast.org/latest/user_guides/forecasting-sarimax-arima.html\n\n\n# Examples and tutorials\n\nExplore our extensive list of examples and tutorials (English and Spanish) to get you started with skforecast. You can find them [here](https://skforecast.org/latest/examples/examples_english).\n\n\n# How to contribute\n\nPrimarily, 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:\n\n- Submit a bug report or feature request on [GitHub Issues](https://github.com/skforecast/skforecast/issues).\n- Contribute a Jupyter notebook to our [examples](https://skforecast.org/latest/examples/examples_english).\n- Write [unit or integration tests](https://docs.pytest.org/en/latest/) for our project.\n- Answer questions on our issues, Stack Overflow, and elsewhere.\n- Translate our documentation into another language.\n- Write a blog post, tweet, or share our project with others.\n\nFor more information on how to contribute to skforecast, see our [Contribution Guide](https://github.com/skforecast/skforecast/blob/master/CONTRIBUTING.md).\n\nVisit our [authors section](https://skforecast.org/latest/authors/authors) to meet all the contributors to skforecast.\n\n\n# Citation\n\nIf you use skforecast for a scientific publication, we would appreciate citations to the published software.\n\n**Zenodo**\n\n```\nAmat Rodrigo, Joaquin, & Escobar Ortiz, Javier. (2024). skforecast (v0.14.0). Zenodo. https://doi.org/10.5281/zenodo.8382788\n```\n\n**APA**:\n```\nAmat Rodrigo, J., & Escobar Ortiz, J. (2024). skforecast (Version 0.14.0) [Computer software]. https://doi.org/10.5281/zenodo.8382788\n```\n\n**BibTeX**:\n```\n@software{skforecast,\nauthor = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},\ntitle = {skforecast},\nversion = {0.14.0},\nmonth = {11},\nyear = {2024},\nlicense = {BSD-3-Clause},\nurl = {https://skforecast.org/},\ndoi = {10.5281/zenodo.8382788}\n}\n```\n\nView the [citation file](https://github.com/skforecast/skforecast/blob/master/CITATION.cff).\n\n\n# Donating\n\nIf you found skforecast useful, you can support us with a donation. Your contribution will help to continue developing and improving this project. Many thanks!\n\n<a href=\"https://www.buymeacoffee.com/skforecast\"><img src=\"https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=skforecast&button_colour=f79939&font_colour=000000&font_family=Poppins&outline_colour=000000&coffee_colour=FFDD00\" /></a>\n<br>\n\n\n[](https://www.paypal.com/donate/?hosted_button_id=D2JZSWRLTZDL6)\n\n\n# License\n\n[BSD-3-Clause License](https://github.com/skforecast/skforecast/blob/master/LICENSE)\n",
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