Prediction of Time Series Data Based on Transformer with Soft Dynamic Time Wrapping

被引：2

作者：

Ho, Kuo-Hao ^{[1
]}

Huang, Pei-Shu ^{[1
]}

Wu, I-Chen ^{[1
,2
]}

Wang, Feng-Jian ^{[1
]}

机构：

[1] Natl Chiao Tung Univ, Dept Comp Sci, Hsinchu, Taiwan

[2] Pervas Artificial Intelligence Res PAIR Labs, Hsinchu, Taiwan

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TAIWAN) | 2020年

关键词：

D O I：

10.1109/icce-taiwan49838.2020.9258155

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

It is a challenge to predict the long-term future data from time series data. This paper proposes to use a Transformer with soft dynamic time wrapping for early stopping criteria, called a soft-DTW Transformer. Our experiment in an open-source dataset HouseTwenty shows that the average prediction error rate with soft-DTW Transformer is 27.79%, greatly reduced from 45.70% for using SVR, a common time series method.

引用

页数：2

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