AutoGluon–TimeSeries: AutoML for Probabilistic Time Series ForecastingDownload PDF

Published: 15 Aug 2023, Last Modified: 05 Oct 2023AutoML 2023 (ABCD Track)Readers: Everyone
Keywords: AutoML, forecasting, time series, probabilistic forecasting
TL;DR: AutoGluon-TimeSeries enables users to generate accurate probabilistic time series forecasts in 3 lines of Python code.
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.
Abcd Fit: Applications
Submission Checklist: Yes
Broader Impact Statement: Yes
Paper Availability And License: Yes
Code Of Conduct: Yes
CPU Hours: 6000
GPU Hours: 0
TPU Hours: 0
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