AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
Oleksandr Shchur Caner Turkmen Nick Erickson Huibin Shen Alexander Shirkov Tony Hu Yuyang Wang
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.