AI RESEARCH
Global and Local Topology-Aware Attention with Persistent Homology and Euler Biases for Time-Series Forecasting
arXiv CS.LG
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ArXi:2605.03163v1 Announce Type: new Scientific time series often encode predictive geometric structure, including connectivity, cycles, shell-like geometry, directional changes, and nonlinear neighborhoods, that standard dot-product attention does not explicitly represent. We We evaluate guarded topology-aware variants across three architecture families: lightweight attention/Ridge, PatchTSTForRegression, and TimeSeriesTransformerForPrediction.