Attention-Driven Causal Discovery: From Transformer Matrices to Granger Causal Graphs for Non-Stationary Time-series Data

Causal discovery in non-stationary time series data is crucial for understanding complex systems but remains challenging due to evolving relationships over time. This paper presents a novel two-stage approach for causal discovery in non-stationary multivariate time series data. The first stage employs a Temporal Attention Forecasting Network (TAFNet), a modified Transformer architecture, to capture complex temporal dependencies and generate informative attention matrices. The second stage utilizes these matrices in an iterative process for Granger causality discovery, refining the predicted causal graph while improving forecasting accuracy. The proposed method addresses the limitations of existing approaches and provides a more complete understanding of causal relationships in non-stationary systems. Extensive experiments demonstrate the method’s superior performance compared to state-of-the-art approaches, particularly in handling non-linear relationships and scaling to high-dimensional data.