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We tackle the problem of forecasting network-signal snapshots using pastsignal measurements acquired by a subset of network nodes. This task can beseen as a combination of multivariate time-series prediction and graph-signalinterpolation. This is a fundamental problem for many applications whereindeploying a high granularity network is impractical. Our solution combinesrecurrent neural networks with frequency-analysis tools from graph signalprocessing, and assumes that data is sufficiently smooth with respect to theunderlying graph. The proposed approach outperforms state-of-the-art deeplearning techniques, especially when only a small fraction of the graph signalsis accessible, considering two distinct real world datasets: temperatures inthe US and speed flow in Seattle. The results also indicate that our method canhandle noisy signals and missing data, making it suitable to many practicalapplications.

Joint Forecasting and Interpolation of Graph Signals Using Deep Learning