Joint Forecasting and Interpolation of Graph Signals Using Deep Learning

TL;DR

This paper introduces a deep learning method that combines recurrent neural networks and graph signal processing to forecast and interpolate network signals, especially effective with limited data and noisy signals.

Contribution

It proposes a novel approach integrating RNNs with frequency analysis for graph signals, improving accuracy over existing methods in sparse and noisy data scenarios.

Findings

Outperforms state-of-the-art deep learning techniques.

Effective with small fractions of accessible signals.

Handles noisy signals and missing data well.

Abstract

We tackle the problem of forecasting network-signal snapshots using past signal measurements acquired by a subset of network nodes. This task can be seen as a combination of multivariate time-series prediction and graph-signal interpolation. This is a fundamental problem for many applications wherein deploying a high granularity network is impractical. Our solution combines recurrent neural networks with frequency-analysis tools from graph signal processing, and assumes that data is sufficiently smooth with respect to the underlying graph. The proposed approach outperforms state-of-the-art deep learning techniques, especially when only a small fraction of the graph signals is accessible, considering two distinct real world datasets: temperatures in the US and speed flow in Seattle. The results also indicate that our method can handle noisy signals and missing data, making it suitable to many practical applications.