LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification

• URL: http://arxiv.org/abs/2301.04838v3
• Date: Tue, 5 Sep 2023 18:33:00 GMT
• Title: LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification
• Authors: Wenjie Xi, Arnav Jain, Li Zhang, Jessica Lin
• Abstract summary: We propose a new efficient semi-supervised time series classification technique, LB-SimTSC, with a new graph construction module. We construct the pairwise distance matrix using LB_Keogh and build a graph for the graph neural network. Results demonstrate that this approach can be up to 104x faster than SimTSC when constructing the graph on large datasets.
• Score: 4.7828959446344275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series classification is an important data mining task that has received a lot of interest in the past two decades. Due to the label scarcity in practice, semi-supervised time series classification with only a few labeled samples has become popular. Recently, Similarity-aware Time Series Classification (SimTSC) is proposed to address this problem by using a graph neural network classification model on the graph generated from pairwise Dynamic Time Warping (DTW) distance of batch data. It shows excellent accuracy and outperforms state-of-the-art deep learning models in several few-label settings. However, since SimTSC relies on pairwise DTW distances, the quadratic complexity of DTW limits its usability to only reasonably sized datasets. To address this challenge, we propose a new efficient semi-supervised time series classification technique, LB-SimTSC, with a new graph construction module. Instead of using DTW, we propose to utilize a lower bound of DTW, LB_Keogh, to approximate the dissimilarity between instances in linear time, while retaining the relative proximity relationships one would have obtained via computing DTW. We construct the pairwise distance matrix using LB_Keogh and build a graph for the graph neural network. We apply this approach to the ten largest datasets from the well-known UCR time series classification archive. The results demonstrate that this approach can be up to 104x faster than SimTSC when constructing the graph on large datasets without significantly decreasing classification accuracy.

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