| Name | skforecast JSON |
| Version |
0.11.0
JSON |
| download |
| home_page | |
| Summary | Forecasting time series with scikit-learn regressors. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM, XGBoost, Ranger...). |
| upload_time | 2023-11-16 21:34:59 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | BSD 3-Clause License Copyright (c) 2023, 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 |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<h1 align="left">
<img src="https://github.com/JoaquinAmatRodrigo/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://github.com/JoaquinAmatRodrigo/skforecast/graphs/commit-activity) [](https://www.repostatus.org/#active) |
| Meta | [](https://github.com/JoaquinAmatRodrigo/skforecast/blob/master/LICENSE) [](https://zenodo.org/doi/10.5281/zenodo.8382787) |
| Testing | [](https://github.com/JoaquinAmatRodrigo/skforecast/actions/workflows/unit-tests.yml/badge.svg) [](https://codecov.io/gh/JoaquinAmatRodrigo/skforecast) |
|Donation | [](https://www.paypal.com/donate/?hosted_button_id=D2JZSWRLTZDL6) [](https://www.buymeacoffee.com/skforecast) 
# About The Project
**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 (LightGBM, XGBoost, CatBoost, ...).
**Why use skforecast?**
The fields of statistics and machine learning have developed many excellent regression algorithms that can be useful for forecasting, but applying them effectively to time series analysis can still be a challenge. To address this issue, the skforecast library provides a comprehensive set of tools for training, validation and prediction in a variety of scenarios commonly encountered when working with time series. The library is built using the widely used scikit-learn API, making it easy to integrate into existing workflows. With skforecast, users have access to a wide range of functionalities such as feature engineering, model selection, hyperparameter tuning and many others. This allows users to focus on the essential aspects of their projects and leave the intricacies of time series analysis to skforecast. In addition, skforecast is developed according to the following priorities:
+ Fast and robust prototyping. :zap:
+ Validation and backtesting methods to have a realistic assessment of model performance. :mag:
+ Models must be deployed in production. :hammer:
+ Models must be interpretable. :crystal_ball:
**Share Your Thoughts with Us**
Thank you for choosing skforecast! We value your suggestions, bug reports and recommendations as they help us identify areas for improvement and ensure that skforecast meets the needs of the community. Please consider sharing your experiences, reporting bugs, making suggestions or even contributing to the codebase on GitHub. Together, let's make time series forecasting more accessible and accurate for 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:
# Installation
The default installation of skforecast only installs hard dependencies.
```bash
pip install skforecast
```
Specific version:
```bash
pip install skforecast==0.11.0
```
Latest (unstable):
```bash
pip install git+https://github.com/JoaquinAmatRodrigo/skforecast#master
```
Install the full version (all dependencies):
```bash
pip install skforecast[full]
```
Install optional dependencies:
```bash
pip install skforecast[sarimax]
```
```bash
pip install skforecast[plotting]
```
# Dependencies
+ Python >= 3.8
## Hard dependencies
+ numpy>=1.20, <1.27
+ pandas>=1.2, <2.2
+ tqdm>=4.57.0, <4.67
+ scikit-learn>=1.0, <1.4
+ optuna>=2.10.0, <3.5
+ joblib>=1.1.0, <1.4
## Optional dependencies
+ matplotlib>=3.3, <3.9
+ seaborn>=0.11, <0.14
+ statsmodels>=0.12, <0.15
+ pmdarima>=2.0, <2.1
# What is new in skforecast 0.11?
Visit the [release notes](https://github.com/JoaquinAmatRodrigo/skforecast/blob/master/changelog.md) to view all notable changes.
- [x] Create `ForecasterBaseline.ForecasterEquivalentDate`, a Forecaster to create simple model that serves as a basic reference for evaluating the performance of more complex models.
- [x] New `predict_quantiles` method in all Autoreg Forecasters to calculate the specified quantiles for each step.
- [x] Added `skforecast.datasets` module. It contains functions to load data for our examples and user guides.
- [x] Enhance the management of internal copying in skforecast to minimize the number of copies, thereby accelerating data processing.
- [x] Bug fixes and performance improvements.
# 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 | Custom features |
|:-----------|:-------------:|:---------------:|:------------------:|:---------------:|:------------------------:|:---------------------------:|:------------------:|:---------------:|
|[ForecasterAutoreg](https://skforecast.org/latest/user_guides/autoregresive-forecaster.html)|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:||
|[ForecasterAutoregCustom](https://skforecast.org/latest/user_guides/custom-predictors.html)|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|
|[ForecasterAutoregDirect](https://skforecast.org/latest/user_guides/direct-multi-step-forecasting.html)|:heavy_check_mark:|||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:||
|[ForecasterMultiSeries](https://skforecast.org/latest/user_guides/independent-multi-time-series-forecasting.html)||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:||
|[ForecasterMultiSeriesCustom](https://skforecast.org/latest/user_guides/custom-predictors.html)||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|
|[ForecasterMultiVariate](https://skforecast.org/latest/user_guides/dependent-multi-series-multivariate-forecasting.html)||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:||
|[ForecasterSarimax](https://skforecast.org/latest/user_guides/forecasting-sarimax-arima.html)|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:||
# Main User Guides
+ [Introduction to time series and forecasting](https://skforecast.org/latest/introduction-forecasting/introduction-forecasting.html)
+ [Recursive multi-step forecasting](https://skforecast.org/latest/user_guides/autoregresive-forecaster.html)
+ [Direct multi-step forecasting](https://skforecast.org/latest/user_guides/direct-multi-step-forecasting.html)
+ [Independent multi-series forecasting](https://skforecast.org/latest/user_guides/independent-multi-time-series-forecasting.html)
+ [Dependent multi-series forecasting (Multivariate forecasting)](https://skforecast.org/latest/user_guides/dependent-multi-series-multivariate-forecasting.html)
+ [Backtesting (validation) of forecasting models](https://skforecast.org/latest/user_guides/backtesting.html)
+ [Hyperparameter tuning and lags selection of forecasting models](https://skforecast.org/latest/user_guides/hyperparameter-tuning-and-lags-selection.html)
+ [Probabilistic forecasting](https://skforecast.org/latest/user_guides/probabilistic-forecasting.html)
+ [Using forecasters in production](https://skforecast.org/latest/user_guides/forecaster-in-production.html)
# Examples and tutorials
**English**
+ [**Skforecast: time series forecasting with Python and Scikit-learn**](https://www.cienciadedatos.net/documentos/py27-time-series-forecasting-python-scikitlearn.html) [](https://colab.research.google.com/drive/1X1DJF4pZlklIt5srQnyTYoyFVLunr_OQ)
+ [**ARIMA and SARIMAX models**](https://www.cienciadedatos.net/documentos/py51-arima-sarimax-models-python.html)
+ [**Forecasting with gradient boosting: skforecast, XGBoost, LightGBM and CatBoost**](https://www.cienciadedatos.net/documentos/py39-forecasting-time-series-with-skforecast-xgboost-lightgbm-catboost.html) [](https://colab.research.google.com/drive/1Imy8ZM3DqPXg7UllRDH9gqWb_XSrqzzh)
+ [**Modelling time series trend with tree based models**](https://www.cienciadedatos.net/documentos/py49-modelling-time-series-trend-with-tree-based-models.html)
+ [**Forecasting electricity demand with Python**](https://www.cienciadedatos.net/documentos/py29-forecasting-electricity-power-demand-python.html) [](https://colab.research.google.com/drive/1efCKQtuHOlw7MLojIwqi2zrU2NZbG-FP)
+ [**Forecasting web traffic with machine learning and Python**](https://www.cienciadedatos.net/documentos/py37-forecasting-web-traffic-machine-learning.html) [](https://colab.research.google.com/drive/1QhLkJAAEfvgYoVkQXy58-T_sloNFCV1o)
+ [**Bitcoin price prediction with Python**](https://www.cienciadedatos.net/documentos/py41-forecasting-cryptocurrency-bitcoin-machine-learning-python.html)
+ [**Prediction intervals in forecasting models**](https://www.cienciadedatos.net/documentos/py42-forecasting-prediction-intervals-machine-learning.html)
+ [**Multi-series forecasting**](https://www.cienciadedatos.net/documentos/py44-multi-series-forecasting-skforecast.html)
+ [**Reducing the influence of Covid-19 on time series forecasting models**](https://www.cienciadedatos.net/documentos/py45-weighted-time-series-forecasting.html)
+ [**Forecasting time series with missing values**](https://www.cienciadedatos.net/documentos/py46-forecasting-time-series-missing-values.html)
+ [**Intermittent demand forecasting**](https://www.cienciadedatos.net/documentos/py48-intermittent-demand-forecasting.html)
**Español**
+ [**Skforecast: forecasting series temporales con Python y Scikit-learn**](https://www.cienciadedatos.net/documentos/py27-forecasting-series-temporales-python-scikitlearn.html) [](https://colab.research.google.com/drive/1mjmccrMA-XxOVXm-3wKSIQ9__oo9dJ5a)
+ [**Modelos ARIMA y SARIMAX**](https://cienciadedatos.net/documentos/py51-modelos-arima-sarimax-python.html)
+ [**Forecasting con gradient boosting: skforecast, XGBoost, LightGBM y CatBoost**](https://www.cienciadedatos.net/documentos/py39-forecasting-series-temporales-con-skforecast-xgboost-lightgbm-catboost.html) [](https://colab.research.google.com/drive/1UAjX8vUKDoY0XJtq5WtHlJ4qwPvSgLrD)
+ [**Modelar series temporales con tendencia utilizando modelos de árboles**](https://cienciadedatos.net/documentos/py49-modelar-tendencia-en-series-temporales-modelos-de-arboles.html)
+ [**Forecasting de la demanda eléctrica**](https://www.cienciadedatos.net/documentos/py29-forecasting-demanda-energia-electrica-python.html) [](https://colab.research.google.com/drive/15kQpANRBCLfNf77nmNcV6GjGPoYdOmmF)
+ [**Forecasting de las visitas a una página web**](https://www.cienciadedatos.net/documentos/py37-forecasting-visitas-web-machine-learning.html) [](https://colab.research.google.com/drive/1uw2nyjA9XMcstfkpbWC4zCULN7Qp7MWV)
+ [**Predicción del precio de Bitcoin con Python**](https://www.cienciadedatos.net/documentos/py41-forecasting-criptomoneda-bitcoin-machine-learning-python.html)
+ [**Workshop predicción de series temporales con machine learning Universidad de Deusto / Deustuko Unibertsitatea**](https://youtu.be/MlktVhReO0E)
+ [**Intervalos de predicción en modelos de forecasting**](https://www.cienciadedatos.net/documentos/py42-intervalos-prediccion-modelos-forecasting-machine-learning.html)
+ [**Multi-series forecasting**](https://www.cienciadedatos.net/documentos/py44-multi-series-forecasting-skforecast-español.html)
+ [**Predicción de demanda intermitente**](https://www.cienciadedatos.net/documentos/py48-forecasting-demanda-intermitente.html)
# 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/JoaquinAmatRodrigo/skforecast/issues).
- Contribute a Jupyter notebook to our [examples](https://skforecast.org/latest/examples/examples).
- 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/JoaquinAmatRodrigo/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. (2023). skforecast (v0.11.0). Zenodo. https://doi.org/10.5281/zenodo.8382788
```
**APA**:
```
Amat Rodrigo, J., & Escobar Ortiz, J. (2023). skforecast (Version 0.11.0) [Computer software]. https://doi.org/10.5281/zenodo.8382788
```
**BibTeX**:
```
@software{skforecast,
author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
title = {skforecast},
version = {0.11.0},
month = {11},
year = {2023},
license = {BSD-3-Clause},
url = {https://skforecast.org/},
doi = {10.5281/zenodo.8382788}
}
```
View the [citation file](https://github.com/JoaquinAmatRodrigo/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/JoaquinAmatRodrigo/skforecast/blob/master/LICENSE)
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"description": "<h1 align=\"left\">\n<img src=\"https://github.com/JoaquinAmatRodrigo/skforecast/blob/master/images/banner-landing-page-skforecast.png?raw=true#only-light\" style= margin-top: 0px;>\n</h1>\n\n\n| | |\n| --- | --- |\n| Package |  [](https://pypi.org/project/skforecast/) [](https://pepy.tech/project/skforecast) [](https://github.com/JoaquinAmatRodrigo/skforecast/graphs/commit-activity) [](https://www.repostatus.org/#active) |\n| Meta | [](https://github.com/JoaquinAmatRodrigo/skforecast/blob/master/LICENSE) [](https://zenodo.org/doi/10.5281/zenodo.8382787) |\n| Testing | [](https://github.com/JoaquinAmatRodrigo/skforecast/actions/workflows/unit-tests.yml/badge.svg) [](https://codecov.io/gh/JoaquinAmatRodrigo/skforecast) |\n|Donation | [](https://www.paypal.com/donate/?hosted_button_id=D2JZSWRLTZDL6) [](https://www.buymeacoffee.com/skforecast) \n\n\n\n# About The Project\n\n**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 (LightGBM, XGBoost, CatBoost, ...).\n\n**Why use skforecast?**\n\nThe fields of statistics and machine learning have developed many excellent regression algorithms that can be useful for forecasting, but applying them effectively to time series analysis can still be a challenge. To address this issue, the skforecast library provides a comprehensive set of tools for training, validation and prediction in a variety of scenarios commonly encountered when working with time series. The library is built using the widely used scikit-learn API, making it easy to integrate into existing workflows. With skforecast, users have access to a wide range of functionalities such as feature engineering, model selection, hyperparameter tuning and many others. This allows users to focus on the essential aspects of their projects and leave the intricacies of time series analysis to skforecast. In addition, skforecast is developed according to the following priorities:\n\n+ Fast and robust prototyping. :zap:\n+ Validation and backtesting methods to have a realistic assessment of model performance. :mag:\n+ Models must be deployed in production. :hammer:\n+ Models must be interpretable. :crystal_ball:\n\n**Share Your Thoughts with Us**\n\nThank you for choosing skforecast! We value your suggestions, bug reports and recommendations as they help us identify areas for improvement and ensure that skforecast meets the needs of the community. Please consider sharing your experiences, reporting bugs, making suggestions or even contributing to the codebase on GitHub. Together, let's make time series forecasting more accessible and accurate for 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\n# Installation\n\nThe default installation of skforecast only installs hard dependencies.\n\n```bash\npip install skforecast\n```\n\nSpecific version:\n\n```bash\npip install skforecast==0.11.0\n```\n\nLatest (unstable):\n\n```bash\npip install git+https://github.com/JoaquinAmatRodrigo/skforecast#master\n```\n\nInstall the full version (all dependencies):\n\n```bash\npip install skforecast[full]\n```\n\nInstall optional dependencies:\n\n```bash\npip install skforecast[sarimax]\n```\n\n```bash\npip install skforecast[plotting]\n```\n\n# Dependencies\n\n+ Python >= 3.8\n\n## Hard dependencies\n\n+ numpy>=1.20, <1.27\n+ pandas>=1.2, <2.2\n+ tqdm>=4.57.0, <4.67\n+ scikit-learn>=1.0, <1.4\n+ optuna>=2.10.0, <3.5\n+ joblib>=1.1.0, <1.4\n\n## Optional dependencies\n\n+ matplotlib>=3.3, <3.9\n+ seaborn>=0.11, <0.14\n+ statsmodels>=0.12, <0.15\n+ pmdarima>=2.0, <2.1\n\n# What is new in skforecast 0.11?\n\nVisit the [release notes](https://github.com/JoaquinAmatRodrigo/skforecast/blob/master/changelog.md) to view all notable changes.\n\n- [x] Create `ForecasterBaseline.ForecasterEquivalentDate`, a Forecaster to create simple model that serves as a basic reference for evaluating the performance of more complex models.\n- [x] New `predict_quantiles` method in all Autoreg Forecasters to calculate the specified quantiles for each step.\n- [x] Added `skforecast.datasets` module. It contains functions to load data for our examples and user guides.\n- [x] Enhance the management of internal copying in skforecast to minimize the number of copies, thereby accelerating data processing.\n- [x] Bug fixes and performance improvements.\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 | Custom features |\n|:-----------|:-------------:|:---------------:|:------------------:|:---------------:|:------------------------:|:---------------------------:|:------------------:|:---------------:|\n|[ForecasterAutoreg](https://skforecast.org/latest/user_guides/autoregresive-forecaster.html)|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:||\n|[ForecasterAutoregCustom](https://skforecast.org/latest/user_guides/custom-predictors.html)|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:|\n|[ForecasterAutoregDirect](https://skforecast.org/latest/user_guides/direct-multi-step-forecasting.html)|:heavy_check_mark:|||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:||\n|[ForecasterMultiSeries](https://skforecast.org/latest/user_guides/independent-multi-time-series-forecasting.html)||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:||\n|[ForecasterMultiSeriesCustom](https://skforecast.org/latest/user_guides/custom-predictors.html)||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|\n|[ForecasterMultiVariate](https://skforecast.org/latest/user_guides/dependent-multi-series-multivariate-forecasting.html)||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:||:heavy_check_mark:||\n|[ForecasterSarimax](https://skforecast.org/latest/user_guides/forecasting-sarimax-arima.html)|:heavy_check_mark:||:heavy_check_mark:||:heavy_check_mark:|:heavy_check_mark:|:heavy_check_mark:||\n\n\n# Main User Guides\n\n+ [Introduction to time series and forecasting](https://skforecast.org/latest/introduction-forecasting/introduction-forecasting.html)\n\n+ [Recursive multi-step forecasting](https://skforecast.org/latest/user_guides/autoregresive-forecaster.html)\n\n+ [Direct multi-step forecasting](https://skforecast.org/latest/user_guides/direct-multi-step-forecasting.html)\n\n+ [Independent multi-series forecasting](https://skforecast.org/latest/user_guides/independent-multi-time-series-forecasting.html)\n\n+ [Dependent multi-series forecasting (Multivariate forecasting)](https://skforecast.org/latest/user_guides/dependent-multi-series-multivariate-forecasting.html)\n\n+ [Backtesting (validation) of forecasting models](https://skforecast.org/latest/user_guides/backtesting.html)\n\n+ [Hyperparameter tuning and lags selection of forecasting models](https://skforecast.org/latest/user_guides/hyperparameter-tuning-and-lags-selection.html)\n\n+ [Probabilistic forecasting](https://skforecast.org/latest/user_guides/probabilistic-forecasting.html)\n\n+ [Using forecasters in production](https://skforecast.org/latest/user_guides/forecaster-in-production.html)\n\n\n# Examples and tutorials\n\n**English**\n\n+ [**Skforecast: time series forecasting with Python and Scikit-learn**](https://www.cienciadedatos.net/documentos/py27-time-series-forecasting-python-scikitlearn.html) [](https://colab.research.google.com/drive/1X1DJF4pZlklIt5srQnyTYoyFVLunr_OQ)\n\n+ [**ARIMA and SARIMAX models**](https://www.cienciadedatos.net/documentos/py51-arima-sarimax-models-python.html)\n\n+ [**Forecasting with gradient boosting: skforecast, XGBoost, LightGBM and CatBoost**](https://www.cienciadedatos.net/documentos/py39-forecasting-time-series-with-skforecast-xgboost-lightgbm-catboost.html) [](https://colab.research.google.com/drive/1Imy8ZM3DqPXg7UllRDH9gqWb_XSrqzzh)\n\n+ [**Modelling time series trend with tree based models**](https://www.cienciadedatos.net/documentos/py49-modelling-time-series-trend-with-tree-based-models.html)\n\n+ [**Forecasting electricity demand with Python**](https://www.cienciadedatos.net/documentos/py29-forecasting-electricity-power-demand-python.html) [](https://colab.research.google.com/drive/1efCKQtuHOlw7MLojIwqi2zrU2NZbG-FP)\n\n+ [**Forecasting web traffic with machine learning and Python**](https://www.cienciadedatos.net/documentos/py37-forecasting-web-traffic-machine-learning.html) [](https://colab.research.google.com/drive/1QhLkJAAEfvgYoVkQXy58-T_sloNFCV1o)\n\n+ [**Bitcoin price prediction with Python**](https://www.cienciadedatos.net/documentos/py41-forecasting-cryptocurrency-bitcoin-machine-learning-python.html)\n\n+ [**Prediction intervals in forecasting models**](https://www.cienciadedatos.net/documentos/py42-forecasting-prediction-intervals-machine-learning.html)\n\n+ [**Multi-series forecasting**](https://www.cienciadedatos.net/documentos/py44-multi-series-forecasting-skforecast.html)\n\n+ [**Reducing the influence of Covid-19 on time series forecasting models**](https://www.cienciadedatos.net/documentos/py45-weighted-time-series-forecasting.html)\n\n+ [**Forecasting time series with missing values**](https://www.cienciadedatos.net/documentos/py46-forecasting-time-series-missing-values.html)\n\n+ [**Intermittent demand forecasting**](https://www.cienciadedatos.net/documentos/py48-intermittent-demand-forecasting.html)\n\n\n**Espa\u00f1ol**\n\n+ [**Skforecast: forecasting series temporales con Python y Scikit-learn**](https://www.cienciadedatos.net/documentos/py27-forecasting-series-temporales-python-scikitlearn.html) [](https://colab.research.google.com/drive/1mjmccrMA-XxOVXm-3wKSIQ9__oo9dJ5a)\n\n+ [**Modelos ARIMA y SARIMAX**](https://cienciadedatos.net/documentos/py51-modelos-arima-sarimax-python.html)\n\n+ [**Forecasting con gradient boosting: skforecast, XGBoost, LightGBM y CatBoost**](https://www.cienciadedatos.net/documentos/py39-forecasting-series-temporales-con-skforecast-xgboost-lightgbm-catboost.html) [](https://colab.research.google.com/drive/1UAjX8vUKDoY0XJtq5WtHlJ4qwPvSgLrD)\n\n+ [**Modelar series temporales con tendencia utilizando modelos de \u00e1rboles**](https://cienciadedatos.net/documentos/py49-modelar-tendencia-en-series-temporales-modelos-de-arboles.html)\n\n+ [**Forecasting de la demanda el\u00e9ctrica**](https://www.cienciadedatos.net/documentos/py29-forecasting-demanda-energia-electrica-python.html) [](https://colab.research.google.com/drive/15kQpANRBCLfNf77nmNcV6GjGPoYdOmmF)\n\n+ [**Forecasting de las visitas a una p\u00e1gina web**](https://www.cienciadedatos.net/documentos/py37-forecasting-visitas-web-machine-learning.html) [](https://colab.research.google.com/drive/1uw2nyjA9XMcstfkpbWC4zCULN7Qp7MWV)\n\n+ [**Predicci\u00f3n del precio de Bitcoin con Python**](https://www.cienciadedatos.net/documentos/py41-forecasting-criptomoneda-bitcoin-machine-learning-python.html)\n\n+ [**Workshop predicci\u00f3n de series temporales con machine learning Universidad de Deusto / Deustuko Unibertsitatea**](https://youtu.be/MlktVhReO0E)\n\n+ [**Intervalos de predicci\u00f3n en modelos de forecasting**](https://www.cienciadedatos.net/documentos/py42-intervalos-prediccion-modelos-forecasting-machine-learning.html)\n\n+ [**Multi-series forecasting**](https://www.cienciadedatos.net/documentos/py44-multi-series-forecasting-skforecast-espa\u00f1ol.html)\n\n+ [**Predicci\u00f3n de demanda intermitente**](https://www.cienciadedatos.net/documentos/py48-forecasting-demanda-intermitente.html)\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/JoaquinAmatRodrigo/skforecast/issues).\n- Contribute a Jupyter notebook to our [examples](https://skforecast.org/latest/examples/examples).\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/JoaquinAmatRodrigo/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. (2023). skforecast (v0.11.0). Zenodo. https://doi.org/10.5281/zenodo.8382788\n```\n\n**APA**:\n```\nAmat Rodrigo, J., & Escobar Ortiz, J. (2023). skforecast (Version 0.11.0) [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 = {0.11.0},\nmonth = {11},\nyear = {2023},\nlicense = {BSD-3-Clause},\nurl = {https://skforecast.org/},\ndoi = {10.5281/zenodo.8382788}\n}\n```\n\nView the [citation file](https://github.com/JoaquinAmatRodrigo/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/JoaquinAmatRodrigo/skforecast/blob/master/LICENSE)\n",
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"license": "BSD 3-Clause License Copyright (c) 2023, 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. ",
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