TapNet: Multivariate Time Series Classification with Attentional Prototypical Network


  • Xuchao Zhang Virginia Tech
  • Yifeng Gao George Mason University
  • Jessica Lin George Mason University
  • Chang-Tien Lu Virginia Tech




With the advance of sensor technologies, the Multivariate Time Series classification (MTSC) problem, perhaps one of the most essential problems in the time series data mining domain, has continuously received a significant amount of attention in recent decades. Traditional time series classification approaches based on Bag-of-Patterns or Time Series Shapelet have difficulty dealing with the huge amounts of feature candidates generated in high-dimensional multivariate data but have promising performance even when the training set is small. In contrast, deep learning based methods can learn low-dimensional features efficiently but suffer from a shortage of labelled data. In this paper, we propose a novel MTSC model with an attentional prototype network to take the strengths of both traditional and deep learning based approaches. Specifically, we design a random group permutation method combined with multi-layer convolutional networks to learn the low-dimensional features from multivariate time series data. To handle the issue of limited training labels, we propose a novel attentional prototype network to train the feature representation based on their distance to class prototypes with inadequate data labels. In addition, we extend our model into its semi-supervised setting by utilizing the unlabeled data. Extensive experiments on 18 datasets in a public UEA Multivariate time series archive with eight state-of-the-art baseline methods exhibit the effectiveness of the proposed model.




How to Cite

Zhang, X., Gao, Y., Lin, J., & Lu, C.-T. (2020). TapNet: Multivariate Time Series Classification with Attentional Prototypical Network. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6845-6852. https://doi.org/10.1609/aaai.v34i04.6165



AAAI Technical Track: Machine Learning