Sanghun Lee  Hong-Jun Jang  Yang-Sae Moon

10.5626/JOK.2025.52.10.879

feature embedding GraphSAGE node classification Time series Classification time series graph

With the recent increase in graph research, studies analyzing time series within the graph domain have emerged. SimTSC is a novel approach that transforms time series into graphs for node classification; however, it does not account for the relationships between nodes in its feature embedding. To address this issue, we propose SbCM(SAGE-based Classification Model), which performs node feature embedding using GraphSAGE. Additionally, we introduce a new graph construction strategy for neighbor node selection in GraphSAGE, utilizing the original time series. GraphSAGE is an embedding model that leverages information from neighboring nodes, facilitating feature embedding by considering both the target node and its neighbors. Experimental comparisons using UCR data demonstrate that the proposed SbCM achieves up to 2.5 times better classification performance than SimTSC in large-scale data and multiple class scenarios.