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
@article{arxiv.2308.05566,
title = {AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting},
author = {Oleksandr Shchur and Caner Turkmen and Nick Erickson and Huibin Shen and Alexander Shirkov and Tony Hu and Yuyang Wang},
journal= {arXiv preprint arXiv:2308.05566},
year = {2023}
}
Comments
Published at AutoML Conference 2023