GNN Graph Classification In Action: Uncovering Patterns in Time Series Data

Abstract

This study pioneers a technique for analyzing time series data through Graph Neural Network (GNN) Graph Classification, extending its application from the traditional arenas of biology and chemistry to the intricate domain of time series analysis. It elaborates on the process of constructing and examining graphs derived from time series data, utilizing both single graphs and sliding window graphs. This methodology proves effective in discerning complex interrelations within the data. A critical insight from applying GNN Graph Classification to single graphs is the models’ acute sensitivity to graph structures. This attribute significantly enhances their capacity to identify outliers and decode complex patterns that hint at systemic shifts. Such sensitivity underscores the utility of GNN Graph Classification in elucidating time series data dynamics, indicating its broad potential across diverse analytical endeavors. Integrating sliding window techniques with GNN models, our approach emerges as a versatile analysis tool. It adeptly identifies temporal variations and nuanced patterns that elude traditional static analysis techniques. The findings affirm the adaptability and promise of GNN Graph Classification in conducting thorough time series analysis. By spotlighting the models’ structural sensitivity and leveraging advanced sliding window methodologies, this research broadens the application spectrum of GNN Graph Classification in data analysis. It lays the groundwork for future explorations in time series and affiliated domains. Marking a significant leap forward, this study illuminates the capacity of GNN Graph Classification models to dissect and interpret complex data patterns, paving the way for novel time series analysis applications and beyond.