Graph-Based Predictive Coding for Time Series Analysis

Abstract

This paper proposes a novel approach to time series analysis utilizing graph-based predictive coding. Traditional methods often struggle with capturing complex temporal dependencies and lack inherent interpretability. Our framework constructs a graph representation of the time series data, where nodes represent individual time steps and edges encode predictive relationships. We then employ graph neural networks (GNNs) to learn these relationships and generate predictions. The core claim is that this approach improves both the accuracy of predictions and the interpretability of the underlying temporal dynamics. The method's novelty lies in its integration of predictive coding principles with the powerful representation learning capabilities of GNNs. We demonstrate, through theoretical analysis and conceptual explanations, the potential benefits of this approach, outlining the key components and their interactions. The framework allows for a more flexible and nuanced understanding of time series data compared to standard methods.