Time-Varying Signals Recovery Via Graph Neural Networks
- Resource Type
- Conference
- Authors
- Castro-Correa, Jhon A.; Giraldo, Jhony H.; Mondal, Anindya; Badiey, Mohsen; Bouwmans, Thierry; Malliaros, Fragkiskos D.
- Source
- ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on. :1-5 Jun, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Time series analysis
Signal processing algorithms
Filtering algorithms
Transformers
Graph neural networks
Spatiotemporal phenomena
Forecasting
graph signal processing
time-varying graph signal
recovery of signals
- Language
- ISSN
- 2379-190X
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 spatiotemporal 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.