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Multiscale echo self-attention memory network for multivariate time series classification
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DOI：10.1016/j.neucom.2022.11.066.png)
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Abstract
En 中文
Recently, ESN has been applied to time series classification own to its high-dimensional random projec-tion ability and training efficiency characteristic. The major drawback of applying ESN to time series clas-sification 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 differ-ently 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 mem-ory 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.(c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Multi -head self -attention
Echo state network
Long-term dependencies
Multivariate time series classification
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