AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

被引：0

作者：

Shchur, Oleksandr ^{[1
]}

Turkmen, Caner ^{[1
]}

Erickson, Nick ^{[1
]}

Shen, Huibin ^{[2
]}

Shirkov, Alexander ^{[1
]}

Hu, Tony ^{[1
]}

Wang, Yuyang ^{[2
]}

机构：

[1] Amazon Web Serv, Seattle, WA 98109 USA

[2] AWS AI Labs, Seattle, WA USA

来源：

INTERNATIONAL CONFERENCE ON AUTOMATED MACHINE LEARNING, VOL 224 | 2023年 / 224卷

关键词：

EFFICIENT;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce AutoGluon-TimeSeries-an open-source AutoML library for probabilistic time series forecasting.(1) 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.

引用

页数：21