Multiscale echo self-attention memory network for multivariate time series classification

Abstract

Recently, ESN has been applied to time series classification own to its high-dimensional random projection ability and training efficiency characteristic. The major drawback of applying ESN to time series classification is that ESN cannot capture long-term dependency information well. Therefore, the Multiscale Echo Self-Attention Memory Network (MESAMN) is proposed to address this issue. Specifically, the MESAMN consists of a memory encoder and a memory learner. In the memory encoder, multiple differently initialized ESNs are utilized for high-dimensional projection which is then followed by a self-attention mechanism to capture the long-term dependent features. A multiscale convolutional neural network is developed as the memory learner to learn local features using features extracted by the memory encoder. Experimental results show that the proposed MESAMN yields better performance on 18 multivariate time series classification tasks as well as three 3D skeleton-based action recognition tasks compared to existing models. Furthermore, the capacity for capturing long-term dependencies of the MESAMN is verified empirically.