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September 19, 2025Open Access

A graph convolutional network for time series classification using recurrence plots

HKHyewon KangAnalysis Group (United States)TLTaek-Ho LeeSeoul National University of Science and Technology JLJunghye LeeSeoul National University of Science and Technology

Key Points

Improving classification accuracy is achieved by leveraging topological recurrence from recurrence plots and transforming time series into graph structures.
Evaluation on 35 benchmark datasets showed superior accuracy and efficient inference time compared to conventional methods in time series classification.
Graph convolutional networks utilize recurrence relationships effectively by representing time series data as node feature and adjacency matrices.
The study introduces a novel approach to time series representation that addresses limitations of traditional convolutional neural networks.

Abstract

Abstract Time series classification (TSC) is a crucial task across various domains, and its performance heavily depends on the quality of input representations. Among various representations, the recurrence plot (RP) effectively captures topological recurrence, the unique property of time series data. However, conventional convolutional neural networks (CNNs) cannot fully exploit this property since they treat the RP as grid-like data. In this study, we propose RP-GCN, a novel approach that uses a graph convolutional network (GCN) to exploit topological recurrence inherent in the RP, thereby improving TSC performance. Our method transforms a multivariate time series into graphs where state matrices act as node feature matrices and RPs serve as adjacency matrices, enabling graph convolution to utilize recurrence relationships. We evaluated RP-GCN on 35 benchmark multivariate time series classification datasets and demonstrated superior accuracy and efficient inference time compared to existing methods.

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