In this paper, we propose a new end-To-end deep neural network model for time-series classification (TSC) with emphasis on both the accuracy and the interpretation. The proposed model contains a convolutional network component to extract high-level features and a recurrent network component to enhance the modeling of the temporal characteristics of TS data. In addition, a feedforward fully connected network with the sparse group lasso (SGL) regularization is used to generate the final classification. The proposed architecture not only achieves satisfying classification accuracy, but also obtains good interpretability through the SGL regularization. All these networks are connected and jointly trained in an end-To-end framework, and it can be generally applied to TSC tasks across different domains without the efforts of feature engineering. Our experiments in various TS data sets show that the proposed model outperforms the traditional convolutional neural network model for the classification accuracy, and also demonstrate how the SGL contributes to a better model interpretation.

Original languageEnglish (US)
Article number8169670
Pages (from-to)4709-4718
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number10
StatePublished - Oct 2018


  • Convolutional neural network (CNN)
  • deep learning
  • recurrent neural network (RNN)
  • regularization
  • sparse group lasso (SGL)
  • time-series classification (TSC)

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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