Causal Inference Network for Time Series Prediction

Abstract

Traditional time series prediction models often rely on correlation-based approaches, failing to account for the underlying causal relationships that drive the observed patterns. This paper introduces a novel framework, the Causal Inference Network (CIN), designed to address this limitation. The CIN constructs a network representing causal relationships within the time series data, leverages graph neural networks (GNNs) for learning, and employs this network to improve prediction accuracy and robustness. The core claim is that incorporating causal information is crucial for effective time series prediction. The proposed mechanism utilizes a GNN to learn representations from the causal network, ultimately enhancing predictive performance. We demonstrate the effectiveness of the CIN approach through a theoretical analysis and outline a potential implementation strategy. The key contribution lies in explicitly modeling causality within the prediction process, moving beyond purely statistical correlations.