.png){kind=icon}
.png){kind=icon}
Return
A graph convolutional network for time series classification using recurrence plots
.png){kind=icon}
OA
AI
DOI：10.1007/s10489-025-06841-3.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Multivariate time series classification
Recurrence plot
Graph convolutional network
Topological recurrence
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.5
Papers:
7.5K
Citations:
1.7W
View more organizations