Efficient and Accurate Similarity-Aware Graph Neural Network for Semi-supervised Time Series Classification

Semi-supervised time series classification has become an increasingly popular task due to the limited availability of labeled data in practice. Recently, Similarity-aware Time Series Classification (SimTSC) has been proposed to address the label scarcity problem by using a graph neural network on the graph generated from pairwise Dynamic Time Warping (DTW) distance of batch data. While demonstrating superior accuracy compared to the state-of-the-art deep learning models, SimTSC relies on pairwise DTW distance computation and thus has limited usability in practice due to the quadratic complexity of DTW. To address this challenge, we propose a novel efficient semi-supervised time series classification technique with a new graph construction module. Instead of computing the full DTW distance matrix, we propose to approximate the dissimilarity between instances in linear time using a lower bound, while retaining the relative proximity relationships one would have obtained via DTW. The experiments conducted on the ten largest datasets from the UCR archive demonstrate that our model can be up to 104x faster than SimTSC when constructing the graph on large datasets without significantly decreasing classification accuracy.