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

TL;DR

ReGEN-TAD is proposed, an interpretable generative framework that integrates modern machine learning with econometric diagnostics for anomaly detection and 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.

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.

Figures (11)

• Figure 1: Rolling-window construction. The model ingests past $L$ observations $\mathbf{X}_t$ and jointly predicts the future block $\mathbf{F}_t$ over horizon $H$.
• Figure 2: Temporal Convolution over the Input Window. A convolutional filter of width three is applied to the first three time steps of $\mathbf{X}_t$, operating jointly across all $p$ features. The shaded region represents the receptive field used to produce a feature vector $\mathbf{h}_{t-L+3} \in \mathbb{R}^{d}$.
• Figure 3: Positional encoding. The embedding matrix $\mathbf{H}_t$ is augmented by the deterministic positional encoding matrix $\mathbf{P}$ through element-wise addition, producing $\tilde{\mathbf{H}}_t$ while preserving dimensionality.
• Figure 4: Illustration of ReGEN-TAD across six synthetic financial regimes: Bull Market, Mean Shift, Volatility Spike, Flash Crash, Regime Switch, and Liquidity Dryup. Top panels show multivariate price trajectories (sample of first 5 series) with anomaly region shaded. Bottom panels show corresponding ReGEN-TAD anomaly scores and predicted anomalies.
• Figure 5: Overall architecture of ReGEN-TAD. The generative backbone transforms input windows through convolutional, positional, attention-based, and recurrent encoding to produce a latent representation. Forecasting and reconstruction heads generate diagnostic signals, which are aggregated in the decision function using robust scaling and smoothing before applying rank- or threshold-based anomaly selection.