Multidimensional time series classification with multiple attention mechanism

Abstract

The classification of multidimensional time series holds significant importance across various domains, including action classification, medical diagnosis, and credit assessment. Within multidimensional time series data, features pertinent to classification exhibit variance in their positional distribution along the entirety of the sequence. Moreover, the relative significance of features across distinct dimensions also fluctuates, contributing to suboptimal performance in multidimensional time series classification. Consequently, the proposition of tailored deep learning models for feature extraction specific to multidimensional time series data becomes imperative. This paper introduces attention mechanisms applied to the temporal dimension, graph attention mechanisms for inter-dimensional relationships within multidimensional data, and attention mechanisms applied between channels post-convolutional calculations. These mechanisms are deployed for feature extraction across temporal, variational, and channel dimensions of multidimensional time series data, respectively. Furthermore, attention is directed towards inter-channel interactions within the squeeze-and-excitation network to enhance the model’s representational capacity. Experimental findings substantiate the viability of integrating attention mechanisms into multidimensional time series classification endeavors.