Time-varying Signals Recovery via Graph Neural Networks
This addresses a fundamental issue in sensor networks and time series forecasting, but it is incremental as it builds on prior methods with a learning module.
The paper tackles the problem of recovering time-varying graph signals by relaxing the smoothness assumption of temporal differences, proposing TimeGNN, which shows competitive performance on real datasets.
The recovery of time-varying graph signals is a fundamental problem with numerous applications in sensor networks and forecasting in time series. Effectively capturing the spatio-temporal information in these signals is essential for the downstream tasks. Previous studies have used the smoothness of the temporal differences of such graph signals as an initial assumption. Nevertheless, this smoothness assumption could result in a degradation of performance in the corresponding application when the prior does not hold. In this work, we relax the requirement of this hypothesis by including a learning module. We propose a Time Graph Neural Network (TimeGNN) for the recovery of time-varying graph signals. Our algorithm uses an encoder-decoder architecture with a specialized loss composed of a mean squared error function and a Sobolev smoothness operator.TimeGNN shows competitive performance against previous methods in real datasets.