Ego-Network Transformer for Subsequence Classification in Time Series
  Data
Chin-Chia Michael Yeh Huiyuan Chen Yujie Fan Xin Dai Yan Zheng Vivian Lai Junpeng Wang Zhongfang Zhuang Liang Wang Wei Zhang Eamonn Keogh
Time series classification is a widely studied problem in the field of time
series data mining. Previous research has predominantly focused on scenarios
where relevant or foreground subsequences have already been extracted, with
each subsequence corresponding to a single label. However, real-world time
series data often contain foreground subsequences that are intertwined with
background subsequences. Successfully classifying these relevant subsequences
requires not only distinguishing between different classes but also accurately
identifying the foreground subsequences amidst the background. To address this
challenge, we propose a novel subsequence classification method that represents
each subsequence as an ego-network, providing crucial nearest neighbor
information to the model. The ego-networks of all subsequences collectively
form a time series subsequence graph, and we introduce an algorithm to
efficiently construct this graph. Furthermore, we have demonstrated the
significance of enforcing temporal consistency in the prediction of adjacent
subsequences for the subsequence classification problem. To evaluate the
effectiveness of our approach, we conducted experiments using 128 univariate
and 30 multivariate time series datasets. The experimental results demonstrate
the superior performance of our method compared to alternative approaches.
Specifically, our method outperforms the baseline on 104 out of 158 datasets.