skforecast 0.7.0

Forecasting time series with scikit-learn regressors. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM, XGBoost, Ranger...).

skforecast

Time series forecasting with scikit-learn regressors.

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM, XGBoost, Ranger...).

Why use Skforecast?

Skforecast is developed according to the following priorities:

• Fast and robust prototyping.
• Validation and backtesting methods to have a realistic assessment of model performance.
• Models must be deployed in production.
• Models must be interpretable.

Documentation: https://joaquinamatrodrigo.github.io/skforecast/

Installation

The default installation of skforecast only installs hard dependencies.

pip install skforecast

Specific version:

pip install skforecast==0.7.0

Latest (unstable):

pip install git+https://github.com/JoaquinAmatRodrigo/skforecast#master

Install the full version (all dependencies):

pip install skforecast[full]

Install optional dependencies:

pip install skforecast[sarimax]

pip install skforecast[plotting]

Dependencies

• Python >= 3.8

Hard dependencies

• numpy>=1.20, <1.25
• pandas>=1.2, <1.6
• tqdm>=4.57.0, <4.65
• scikit-learn>=1.0, <1.3
• optuna>=2.10.0, <3.2
• joblib>=1.1.0, <1.3.0

Optional dependencies

• matplotlib>=3.3, <3.8
• seaborn>=0.11, <0.13
• statsmodels>=0.12, <0.14
• pmdarima>=2.0, <2.1

Features

• Create recursive autoregressive forecasters from any regressor that follows the scikit-learn API
• Create direct autoregressive forecasters from any regressor that follows the scikit-learn API
• Create multi-series autoregressive forecasters from any regressor that follows the scikit-learn API
• Include exogenous variables as predictors
• Include custom predictors (rolling mean, rolling variance ...)
• Multiple backtesting methods for model validation
• Grid search, random search and bayesian search to find optimal lags (predictors) and best hyperparameters
• Include custom metrics for model validation and grid search
• Prediction interval estimated by bootstrapping and quantile regression
• Get predictor importance
• Forecaster in production

What is new in skforecast 0.7.0?

• Predict and plot the distribution of each predicted step, predict_dist and plot_prediction_distribution (Ridgeline plot).
• A class for ARIMA models with pmdarima, ForecasterSarimax, model_selection_sarimax.
• Modeling multiple time series simultaneously with a custom function to create predictors, ForecasterAutoregMultiSeriesCustom.
• python 3.11 compatibility.
• Bug fixes and performance improvements.

Visit the changelog to view all notable changes.

Documentation

The documentation for the latest release is at skforecast docs.

Recent improvements are highlighted in the release notes.

• Introduction to time series and forecasting

• Recursive multi-step forecasting

• Multi-time series forecasting

• Multivariate forecasting

• Backtesting (validation) of forecasting models

• Hyperparameter tuning and lags selection of forecasting models

• Prediction intervals

• Using forecasters in production

Examples and tutorials

English

• Skforecast: time series forecasting with Python and Scikit-learn

• Forecasting electricity demand with Python

• Forecasting web traffic with machine learning and Python

• Forecasting time series with gradient boosting: Skforecast, XGBoost, LightGBM and CatBoost

• Bitcoin price prediction with Python

• Prediction intervals in forecasting models

• Multi-series forecasting

• Reducing the influence of Covid-19 on time series forecasting models

• Forecasting time series with missing values

Español

• Skforecast: forecasting series temporales con Python y Scikit-learn

• Forecasting de la demanda eléctrica

• Forecasting de las visitas a una página web

• Forecasting series temporales con gradient boosting: Skforecast, XGBoost, LightGBM y CatBoost

• Predicción del precio de Bitcoin con Python

• Workshop predicción de series temporales con machine learning Universidad de Deusto / Deustuko Unibertsitatea

• Intervalos de predicción en modelos de forecasting

• Multi-series forecasting

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!

Citation

If you use this software, please cite it using the following metadata.

APA:

Amat Rodrigo, J., & Escobar Ortiz, J. skforecast (Version 0.7.0) [Computer software]

BibTeX:

@software{skforecast,
author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
license = {MIT},
month = {3},
title = {{skforecast}},
version = {0.7.0},
year = {2023}
}

View the citation file.

License

joaquinAmatRodrigo/skforecast is licensed under the MIT License, a short and simple permissive license with conditions only requiring the preservation of copyright and license notices. Licensed works, modifications and larger works may be distributed under different terms and without source code.