AINeutralarXiv – CS AI · Jun 256/10
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TopoCast: A Topological Fidelity Framework for Evaluating Transformer-Based Time Series Forecasting
Researchers introduce TopoCast, a topology-based evaluation framework for time series forecasting that moves beyond traditional error metrics to assess structural fidelity in deep learning models. The framework uses persistent homology to detect phase shifts, oscillatory distortions, and timing errors that conventional metrics like MSE overlook, revealing that models with similar numerical accuracy can exhibit substantially different structural quality.