AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

• URL: http://arxiv.org/abs/2308.05566v1
• Date: Thu, 10 Aug 2023 13:28:59 GMT
• Title: AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
• Authors: Oleksandr Shchur, Caner Turkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Yuyang Wang
• Abstract summary: AutoGluon-TimeSeries is an open-source AutoML library for probabilistic time series forecasting. It generates accurate point and quantile forecasts with just 3 lines of Python code.
• Score: 80.14147131520556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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