AutoGluon–TimeSeries: AutoML for Probabilistic Time Series Forecasting

Oleksandr Shchur, Ali Caner Turkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Bernie Wang

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

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/autogluon-timeseries-automl-for-probabilistic/code)