DSNet: A Dual-Scaled Network for Multivariate Time Series Classification with Learning Temporal Features

Abstract Multivariate time series classification (MTSC) is a widely recognized and important task in machine learning. Current methods often fail to account for the correlation between holistic and local temporal features. In this paper, we propose a novel dual-scaled network called DSNet, which combines holistic and local features and merges temporal information through a dual-scaled gating mechanism. The DSNet model's holistic module introduces the encoder part of the native Transformer, while the local module mainly consists of our proposed residual convolution module (ResBlock) and Squeeze-And-Excite Block to extract local features. Our experimental results show that our proposed method achieves higher accuracy on 12 out of the 20 UEA MTSC archive datasets. Furthermore, our method has a higher average ranking of 1.55 compared to the other 7 state-of-the-art methods.