Journal
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS
Volume 6, Issue -, Pages 761-773Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSIPN.2020.3040042
Keywords
Multivariate time series; forecasting and interpolation; deep learning; recurrent neural networks (RNNs); graph signal processing (GSP)
Funding
- ERC project AGNOSTIC (Actively Enhanced Cognition based Framework for Design of Complex Systems) [742648]
- Brazilian agency CNPq
- Brazilian agency Faperj
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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 forecasting (temporal prediction) and graph-signal interpolation (spatial prediction). 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 learning model outperforms state-of-the-art deep learning techniques, especially when predictions are made using a small subset of network nodes, 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.
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