Densely Knowledge-Aware Network for Multivariate Time Series Classification

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted increasingly more research attention. The performance of a DL-based MTSC algorithm is heavily dependent on the quality of the learned representations providing semantic information for downstream tasks, e.g., classification. Hence, a model’s representation learning ability is critical for enhancing its performance. This article proposes a densely knowledge-aware network (DKN) for MTSC. The DKN’s feature extractor consists of a residual multihead convolutional network (ResMulti) and a transformer-based network (Trans), called ResMulti-Trans. ResMulti has five residual multihead blocks for capturing the local patterns of…

• NN
  National Natural Science Foundation of China

  Awards: 62172342, 62202392
• NS
  Natural Science Foundation of Hebei Province

  Award: F2022105027
• NS
  Natural Science Foundation of Sichuan Province

  Awards: 2023NSFSC0459, 2022NSFSC0568, 2022NSFSC0944
• FR
  Fundamental Research Funds for the Central Universities