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

Harsh Poonia, Felix Divo, Kristian Kersting, Devendra Singh Dhami

Keywords: Granger causality, xLSTM, time series

TL;DR: GC-xLSTM jointly optimizes a sparse feature selector and predictive xLSTM to uncover a graph of Granger Causal dependencies

Abstract: 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. Although successful, Granger causal methods still struggle with capturing long-range relations between variables. To this end, we leverage the recently successful Extended Long Short-Term Memory (xLSTM) architecture and propose Granger causal xLSTMs (GC-xLSTM). It first enforces sparsity between the time series components by using a novel dynamic loss penalty on the initial projection. Specifically, we adaptively improve the model and identify sparsity candidates. Our joint optimization procedure then ensures that the Granger causal relations are recovered robustly. Our experimental evaluation on six diverse datasets demonstrates the overall efficacy of GC-xLSTM.

Primary Area: Probabilistic methods (e.g., variational inference, causal inference, Gaussian processes)

Submission Number: 8959