Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data

Causality in time series can be challenging to determine, especially in the presence of non-linear dependencies. Granger causality helps analyze potential relationships between variables, thereby offering a method to determine whether one time series can predict--Granger cause--future values of another.