TCRAN: Multivariate time series classification using residual channel attention networks with time correction

Currently, the most popular and effective approach to solve multivariate time series classification(MTSC) tasks is based on deep learning technology. However, the existing deep learning-based algorithms ignore the unique time characteristics of time series in the process of network training, and do not consider the features correlation in different convolutional layers. So they cannot obtain the convincing feature representation ability and result in unsatisfactory classification accuracy. To solve this problem, we propose a new time corrected residual attention network(TCRAN) which can fully extract the long-term time-dependence information to enhance the discriminative power of the network. The hallmark of TCRAN is that we employ the time residual channel attention block(TRCAB) as the basic structure, which incorporates the adaptive channel feature adjustment mechanism(AFM) and the bi-directional gated recurrent unit(Bi-GRU) into the deep residual structure to adaptively extract time-dependent features. Meanwhile, to integrate the overall dependency information between different layers, we also employ an inter-module adaptive feature adjustment mechanism(IAM) in the TCRAN. The experiments results with 15 multivariate time series datasets illustrate that the proposed TCRAN can achieve the highest average classification accuracy of 0.7276 and improve accuracy by 1.64% compared to the state-of-the-art method. All these verify the effectiveness of TCRAN.