An Interpretable Generative Framework for Anomaly Detection in High-Dimensional Financial Time Series

Waldyn G Martinez

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arXiv · 2603.07864

An Interpretable Generative Framework for Anomaly Detection in High-Dimensional Financial Time Series

Waldyn G Martinez

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Abstract

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. The model combines joint forecasting and reconstruction within a refined convolutional--transformer architecture and aggregates complementary signals capturing predictive inconsistency, reconstruction degradation, latent distortion, and volatility shifts. Robust calibration yields a unified anomaly score without labeled data. Experiments on synthetic and financial panels demonstrate improved robustness to structured deviations while enabling economically coherent factor-level attribution.

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Waldyn G Martinez. 2026-03-09. An Interpretable Generative Framework for Anomaly Detection in High-Dimensional Financial Time Series. https://arxiv.org/abs/2603.07864

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