Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series
This work addresses the low signal-to-noise ratio in complex systems data for time-series prediction, but it appears incremental as it builds on existing GNN methods with a new filtering approach.
The authors tackled the problem of multivariate time-series prediction by integrating a spatial-temporal graph neural network with a matrix filtering module to generate filtered correlation graphs, resulting in superior performance over baselines on a synthetic sales dataset.
We propose an end-to-end architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a matrix filtering module. This module generates filtered (inverse) correlation graphs from multivariate time series before inputting them into a GNN. In contrast with existing sparsification methods adopted in graph neural network, our model explicitly leverage time-series filtering to overcome the low signal-to-noise ratio typical of complex systems data. We present a set of experiments, where we predict future sales from a synthetic time-series sales dataset. The proposed spatial-temporal graph neural network displays superior performances with respect to baseline approaches, with no graphical information, and with fully connected, disconnected graphs and unfiltered graphs.