@inproceedings{4bc7735230ea43039bda85136e643ede,
title = "A multivariate time series classification method based on self-attention",
abstract = "Multivariate Time Series Classification (MTSC) is believed to be a crucial task towards dynamic process recognition and has been widely studied. Recent years, end-to-end MTSC with Convolutional Neural Network (CNN) has gained increasing attention thanks to its ability to integrates local features. However, it remains a significant challenge for CNN to handle global information and long-range dependencies of time series. In this paper, we present a simple and feasible architecture for MTSC to address these problems. Our model benefits from self-attention, which can help CNN directly capture the relationships of time series between two random time steps or variables. Experimental results of the proposed model work on thirty five complex MTSC tasks show its effectiveness and universality that has to outperform existing state-of-the-art (SOTA) model overall. Besides, our model is computationally efficient, and the parsing speed is six hours faster than the current model.",
keywords = "Multivariate time series classification, Self-attention, Temporal Convolutional Network",
author = "Huiwei Lin and Yunming Ye and Leung, \{Ka Cheong\} and Bowen Zhang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2020.; 13th International Conference on Genetic and Evolutionary Computing, ICGEC 2019 ; Conference date: 01-11-2019 Through 03-11-2019",
year = "2020",
doi = "10.1007/978-981-15-3308-2\_54",
language = "英语",
isbn = "9789811533075",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "491--499",
editor = "Jeng-Shyang Pan and Yongquan Liang and Lin, \{Jerry Chun-Wei\} and Shu-Chuan Chu",
booktitle = "Genetic and Evolutionary Computing - Proceedings of the 13th International Conference on Genetic and Evolutionary Computing, 2019",
address = "德国",
}