AutoGluon–TimeSeries: AutoML for Probabilistic Time Series Forecasting
Abstract
We introduce AutoGluon–TimeSeries—an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables users to generate accurate point and quantile forecasts with just 3 lines of Python code. Built on the design philosophy of AutoGluon, AutoGluon–TimeSeries leverages ensembles of diverse forecasting models to deliver high accuracy within a short training time. AutoGluon–TimeSeries combines both conventional statistical models, machine-learning based forecasting approaches, and ensembling techniques. In our evaluation on 29 benchmark datasets, AutoGluon–TimeSeries demonstrates strong empirical performance, outperforming a range of forecasting methods in terms of both point and quantile forecast accuracy, and often even improving upon the best-in-hindsight combination of prior methods.
Cite
Text
Shchur et al. "AutoGluon–TimeSeries: AutoML for Probabilistic Time Series Forecasting." Proceedings of the Second International Conference on Automated Machine Learning, 2023. doi:10.48550/arXiv.2308.05566Markdown
[Shchur et al. "AutoGluon–TimeSeries: AutoML for Probabilistic Time Series Forecasting." Proceedings of the Second International Conference on Automated Machine Learning, 2023.](https://mlanthology.org/automl/2023/shchur2023automl-autogluontimeseries/) doi:10.48550/arXiv.2308.05566BibTeX
@inproceedings{shchur2023automl-autogluontimeseries,
title = {{AutoGluon–TimeSeries: AutoML for Probabilistic Time Series Forecasting}},
author = {Shchur, Oleksandr and Turkmen, Ali Caner and Erickson, Nick and Shen, Huibin and Shirkov, Alexander and Hu, Tony and Wang, Bernie},
booktitle = {Proceedings of the Second International Conference on Automated Machine Learning},
year = {2023},
pages = {9/1-21},
doi = {10.48550/arXiv.2308.05566},
volume = {224},
url = {https://mlanthology.org/automl/2023/shchur2023automl-autogluontimeseries/}
}