Gegenbauer Graph Neural Networks for Time-varying Signal Reconstruction

arXiv:2403.19800v1 Announce Type: new
Abstract: Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from missing data imputation in sensor networks to time-series forecasting. Accurately capturing the spatio-temporal information inherent in these signals is crucial for effectively addressing these tasks. However, existing approaches relying on smoothness assumptions of temporal differences and simple convex optimization techniques have inherent limitations. To address these challenges, we propose a novel approach …

abstract applications arxiv cs.ai cs.lg data eess.sp forecasting graph graph neural networks imputation information machine machine learning networks neural networks processing sensor series signal temporal type