Multivariate Time Series Early Classification with Interpretability Using Deep Learning and Attention Mechanism
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 timeseries 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.
Early classification on time-series Deep neural network Attention
En-Yu Hsu Chien-Liang Liu Vincent S.Tseng
Department of Computer Science,National Chiao Tung University,1001 University Road,Hsinchu 300,Taiwa Department of Industrial Engineering and Management,National Chiao Tung University,1001 University R
国际会议
澳门
英文
541-553
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）