Self-Attention Causal Dilated Convolutional Neural Network for Multivariate Time Series Classification and Its Application

Abstract

Time Series Classification (TSC) in data mining is gradually developing as an important research direction. Many researchers have developed an extensive interest in Multivariate Time Series Classification (MTSC). The Self-Attention Causal Dilated Convolutional Neural Network (SACDCNN) is proposed to address the limitations of existing models that perform poorly on classification tasks. It designs the residual and dense blocks based on Causal Dilated Convolution based on the traditional residual and dense networks that still have superior performance after deepening the network hierarchy and the dependence of time series on long-range information. A Self-Attention mechanism (SA) is also incorporated to extract the internal autocorrelation of time series features. Comparison experiments on 20 benchmark University of California, Riverside (UCR) and University of California, Irvine (UCI) datasets with eight high-performance classification models show that the method can improve the classification accuracy of time series datasets. Finally, it was applied to petroleum logging reservoir recognition, and a comparison experiment was conducted on two wells. The results show that SACDCNN is effective and significantly superior. It overcomes the shortcomings of traditional logging interpretation techniques and improves the efficiency and success rate of oil and gas exploration.