ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification

Authors

  • Guozhong Li Department of Computer Science, Hong Kong Baptist University, Hong Kong
  • Byron Choi Department of Computer Science, Hong Kong Baptist University, Hong Kong
  • Jianliang Xu Department of Computer Science, Hong Kong Baptist University, Hong Kong
  • Sourav S Bhowmick School of Computing Engineering, Nanyang Technological University, Singapore
  • Kwok-Pan Chun Department of Geography, Hong Kong Baptist University, Hong Kong
  • Grace Lai-Hung Wong Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong

Keywords:

Time-Series/Data Streams, Data Stream Mining, Representation Learning, Unsupervised & Self-Supervised Learning

Abstract

Time series shapelets are short discriminative subsequences that recently have been found not only to be accurate but also interpretable for the classification problem of univariate time series (UTS). However, existing work on shapelets selection cannot be applied to multivariate time series classification (MTSC) since the candidate shapelets of MTSC may come from different variables of different lengths and thus cannot be directly compared. To address this challenge, in this paper, we propose a novel model called ShapeNet, which embeds shapelet candidates of different lengths into a unified space for shapelet selection. The network is trained using cluster-wise triplet loss, which considers the distance between anchor and multiple positive (negative) samples and the distance between positive (negative) samples, which are important for convergence. We compute representative and diversified final shapelets rather than directly using all the embeddings for model building to avoid a large fraction of non-discriminative shapelet candidates. We have conducted experiments on ShapeNet with competitive state-of-the-art and benchmark methods using UEA MTS datasets. The results show that the accuracy of ShapeNet is the best of all the methods compared. Furthermore, we illustrate the shapelets’ interpretability with two case studies.

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Published

2021-05-18

How to Cite

Li, G., Choi, B., Xu, J., S Bhowmick, S., Chun, K.-P., & Wong, G. L.-H. (2021). ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 35(9), 8375-8383. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17018

Issue

Section

AAAI Technical Track on Machine Learning II