AI RESEARCH
Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting
arXiv CS.LG
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ArXi:2509.23668v3 Announce Type: replace Time series forecasting occurs in a range of financial applications providing essential decision-making to investors, regulatory institutions, and analysts. Unlike multivariate time series from other domains, stock time series exhibit industry correlation. Exploiting this kind of correlation can improve forecasting accuracy. However, existing methods based on hypergraphs can only capture industry correlation relatively superficially.