AI RESEARCH
An Interpretable Generative Framework for Anomaly Detection in High-Dimensional Financial Time Series
arXiv CS.LG
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ArXi:2603.07864v1 Announce Type: cross Detecting structural instability and anomalies in high-dimensional financial time series is challenging due to complex temporal dependence and evolving cross-sectional structure. We propose ReGEN-TAD, an interpretable generative framework that integrates modern machine learning with econometric diagnostics for anomaly detection.