ITS2Graph: Graph-based generative adversarial learning for imbalanced time series classification

Abstract

Time Series Classification (TSC) is a fundamental task in data mining and often suffers from class imbalance, particularly in real-world applications. Traditional methods often fail to capture high-order intrinsic dependencies among time series, especially when minority class samples are scarce. Effectively mining such associations to improve minority-class representation remains a significant challenge. To address this issue, we propose ITS2Graph, a graph-based generative adversarial learning framework that exploits high-order associations for imbalanced time series classification. An auto-encoder is employed to extract latent representations of time series, based on which pairwise similarities are computed to construct a graph, thereby reformulating TSC as a node classification task. To mitigate class imbalance, a graph generator synthesizes minority-class node features and their topological connections, while a Graph Convolutional Network (GCN) discriminator is trained to distinguish real from generated nodes. Experimental results on 22 real-world time series datasets demonstrate that ITS2Graph outperforms existing algorithms in imbalanced time series classification tasks.