用生成模型检测高维金融时间序列异常，还能解释原因。
An Interpretable Generative Framework for Anomaly Detection in High-Dimensional Financial Time Series
- 融合预测与重构的卷积-注意力架构
- 四类信号融合，实现无标签异常评分
- 可定位到具体因子，适合金融风控场景
由于复杂的时序依赖和不断变化的横截面结构，高维金融时间序列中的结构性不稳定和异常检测极具挑战。我们提出ReGEN-TAD，一种可解释的生成式异常检测框架，将现代机器学习与计量经济学诊断相结合。该模型在优化的卷积-变压器架构中联合进行预测与重构，并聚合捕捉预测不一致、重构退化、潜在空间扭曲和波动率突变的互补信号。稳健校准后，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上的实验表明，该方法对结构性偏差具有更强的鲁棒性，同时支持经济上合理的因子级归因。
原文摘要 · Abstract (English)
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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