Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting
This addresses the challenge of dynamic relational modeling in time series analysis for applications like finance or sensor networks, though it is incremental as it builds on existing graph neural network approaches.
The paper tackles the problem of capturing time-varying correlations in multivariate time series forecasting by learning a fully-connected supra-graph to model high-resolution dependencies, resulting in EV-FGN outperforming state-of-the-art methods on seven real-world datasets.
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements. Considerable recent successful MTS methods are built with graph neural networks (GNNs) due to their essential capacity for relational modeling. However, previous work often used a static graph structure of time-series variables for modeling MTS failing to capture their ever-changing correlations over time. To this end, a fully-connected supra-graph connecting any two variables at any two timestamps is adaptively learned to capture the high-resolution variable dependencies via an efficient graph convolutional network. Specifically, we construct the Edge-Varying Fourier Graph Networks (EV-FGN) equipped with Fourier Graph Shift Operator (FGSO) which efficiently performs graph convolution in the frequency domain. As a result, a high-efficiency scale-free parameter learning scheme is derived for MTS analysis and forecasting according to the convolution theorem. Extensive experiments show that EV-FGN outperforms state-of-the-art methods on seven real-world MTS datasets.