高维金融时间序列中异常检测的可解释生成框架
An Interpretable Generative Framework for Anomaly Detection in High-Dimensional Financial Time Series
AI总结：
ReGEN-TAD通过结合现代机器学习与经济计量诊断，提出了一种可解释的生成框架，用于高维金融时间序列中的异常检测，提升了鲁棒性和经济一致性。
摘要：
在高维金融时间序列中检测结构性不稳定和异常具有挑战性，因为存在复杂的时序依赖性和演变的横截面结构。我们提出了ReGEN-TAD，一个可解释的生成框架，将现代机器学习与经济计量诊断结合，用于异常检测。该模型结合了联合预测和重建，采用改进的卷积-变压器架构，并整合互补信号，捕捉预测不一致、重建退化、潜在扭曲和波动性变化。稳健的校准产生了一个无需标注数据的统一异常评分。在合成和金融面板上的实验表明，改进了对结构偏差的鲁棒性，同时实现了经济上一致的因素层面归因。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
