Multivariate time series early classification with interpretability using deep learning and attention mechanism

En Yu Hsu, Chien-Liang Liu, Vincent Shin-Mu Tseng*

*Corresponding author for this work

研究成果: Conference contribution同行評審

7 引文 斯高帕斯(Scopus)

摘要

Multivariate time-series early classification is an emerging topic in data mining fields with wide applications like biomedicine, finance, manufacturing, etc. Despite of some recent studies on this topic that delivered promising developments, few relevant works can provide good interpretability. In this work, we consider simultaneously the important issues of model performance, earliness, and interpretability to propose a deep-learning framework based on the attention mechanism for multivariate time-series early classification. In the proposed model, we used a deep-learning method to extract the features among multiple variables and capture the temporal relation that exists in multivariate time-series data. Additionally, the proposed method uses the attention mechanism to identify the critical segments related to model performance, providing a base to facilitate the better understanding of the model for further decision making. We conducted experiments on three real datasets and compared with several alternatives. While the proposed method can achieve comparable performance results and earliness compared to other alternatives, more importantly, it can provide interpretability by highlighting the important parts of the original data, rendering it easier for users to understand how the prediction is induced from the data.

原文English
主出版物標題Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
編輯Min-Ling Zhang, Zhi-Hua Zhou, Zhiguo Gong, Qiang Yang, Sheng-Jun Huang
發行者Springer Verlag
頁面541-553
頁數13
ISBN(列印)9783030161415
DOIs
出版狀態Published - 1 一月 2019
事件23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 - Macau, China
持續時間: 14 四月 201917 四月 2019

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11441 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019
國家China
城市Macau
期間14/04/1917/04/19

指紋 深入研究「Multivariate time series early classification with interpretability using deep learning and attention mechanism」主題。共同形成了獨特的指紋。

引用此