GNN Graph Classification for Time Series: A New Perspective on Climate Change Analysis

Abstract

The use of Graph Neural Networks (GNNs) in time series analysis represents a rising field of study, particularly in the context of GNN Graph Classification, a technique traditionally applied in disciplines such as biology and chemistry. Our research repurposes GNN Graph Classification for the analysis of time series for climate data, focusing on two distinct methodologies: the city-graph method, which effectively captures static temporal snapshots, and the sliding window graph method, adept at tracking dynamic temporal changes. This innovative application of GNN Graph Classification within time series data enables the uncovering of nuanced data trends. We demonstrate how GNNs can construct meaningful graphs from time series data, showcasing their versatility across different analytical contexts. A key finding is GNNs’ adeptness at adapting to changes in graph structure, which significantly improves outlier detection. This enhances our understanding of climate patterns and suggests broader applications of GNN Graph Classification in analyzing complex data systems beyond traditional time series analysis. Our research seeks to fill a gap in current studies by providing an examination of GNNs in climate change analysis, highlighting the potential of these methods in capturing and interpreting intricate data trends.