Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data

The proposed Time-Series Causal Neural Network (TS-CausalNN) is a deep learning technique that can discover contemporaneous and lagged causal relations from non-linear and non-stationary time series data without any assumptions about data distribution or model linearity.