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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.
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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?
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:
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.
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 |
To install the basic version of skforecast
with its core dependencies, run:
pip install skforecast
If you want to learn more about the installation process, dependencies and optional features, please refer to the Installation Guide.
Visit the release notes to view all notable changes.
python 3.12
, python 3.8
is no longer supported.ForecasterAutoregMultiSeries
and ForecasterAutoregMultiSeriesCustom
are able to predict series not seen during training. This is useful when the user wants to predict a new series that was not included in the training data.encoding
can be set to None
in Global Forecasters ForecasterAutoregMultiSeries
and ForecasterAutoregMultiSeriesCustom
. This option does not add the encoded series ids to the regressor training matrix.create_predict_X
method in all recursive and direct Forecasters to allow the user to inspect the matrix passed to the predict method of the regressor.metrics
with functions to calculate metrics for time series forecasting such as mean_absolute_scaled_error
and root_mean_squared_scaled_error
. Visit Time Series Forecasting Metrics for more information.add_aggregated_metric
in backtesting_forecaster_multiseries
to include, in addition to the metrics for each level, the aggregated metric of all levels using the average (arithmetic mean), weighted average (weighted by the number of predicted values of each level) or pooling (the values of all levels are pooled and then the metric is calculated).skip_folds
in model_selection
and model_selection_multiseries
functions. It allows the user to skip some folds during backtesting, which can be useful to speed up the backtesting process and thus the hyperparameter search.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 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |||
ForecasterAutoregCustom | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | ||
ForecasterAutoregDirect | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | ||||
ForecasterMultiSeries | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |||
ForecasterMultiSeriesCustom | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | ||
ForecasterMultiVariate | :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: |
English
Forecasting with gradient boosting: XGBoost, LightGBM and CatBoost
Stacking ensemble of machine learning models to improve forecasting
Global Forecasting Models: Comparative Analysis of Single and Multi-Series Forecasting Modeling
Español
Skforecast: forecasting series temporales con Machine Learning
Forecasting con gradient boosting: XGBoost, LightGBM y CatBoost
Modelar series temporales con tendencia utilizando modelos de árboles
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:
For more information on how to contribute to skforecast, see our Contribution Guide.
Visit our authors section to meet all the contributors to skforecast.
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 (v0.13.0). Zenodo. https://doi.org/10.5281/zenodo.8382788
APA:
Amat Rodrigo, J., & Escobar Ortiz, J. (2024). skforecast (Version 0.13.0) [Computer software]. https://doi.org/10.5281/zenodo.8382788
BibTeX:
@software{skforecast,
author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
title = {skforecast},
version = {0.13.0},
month = {8},
year = {2024},
license = {BSD-3-Clause},
url = {https://skforecast.org/},
doi = {10.5281/zenodo.8382788}
}
View the citation file.
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!
FAQs
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.
We found that skforecast demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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