TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data

ArXi:2605.08111v1 Announce Type: cross The widespread availability of complex time series data in various domains such as environmental science, epidemiology, and economics demands robust causal discovery methods that can identify intricate contemporaneous and lagged relationships in non-stationary, nonlinear, and noisy settings. Existing constraint-based methods often rely heavily on conditional independence tests that degrade for limited data samples and complex distributions, while score-based methods impose strong statistical assumptions.