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arXiv · 2308.05566

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

Oleksandr Shchur·Caner Turkmen·Nick Erickson·Huibin Shen·Alexander Shirkov·Tony Hu·Yuyang Wang
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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.

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Oleksandr Shchur, Caner Turkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Yuyang Wang. 2023-08-10. AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting. https://arxiv.org/abs/2308.05566

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