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.

翻译：由于复杂的时间依赖性和不断演化的横截面结构，在高维金融时间序列中检测结构不稳定性和异常具有挑战性。我们提出了ReGEN-TAD，一个可解释的生成框架，它将现代机器学习与计量经济学诊断相结合以进行异常检测。该模型在改进的卷积-Transformer架构中结合了联合预测与重构，并聚合了捕捉预测不一致性、重构退化、潜在失真和波动率变化的互补信号。通过稳健的校准，无需标注数据即可生成统一的异常评分。在合成数据和金融面板数据上的实验表明，该方法对结构化偏差具有更强的鲁棒性，同时能够实现经济意义一致的因素层面归因。