This repository contains the official code for the AAAI-25 paper:
GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality
In this paper, we designed a framework that models spatial dependencies using interpretable causal relationships and detects anomalies through changes in causal patterns. Specifically, we propose a method to dynamically discover Granger causality effects using gradients in nonlinear deep predictors and employ a simple sparsification strategy to obtain a Granger causality graph, detecting anomalies from a causal perspective.

Acknowledgement

We appreciate the following GitHub repos a lot for their valuable code and efforts.

• pytorch-tsmixer (https://github.com/ditschuk/pytorch-tsmixer)