Forecasting Multi-Dimensional Processes Over Graphs

Alberto Natali, Elvin Isufi, Geert Leus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the representation and the processing arise when each time series carries a vector of quantities rather than a scalar one. To tackle this issue, we devise a new framework and propose new methodologies based on the graph vector autoregressive model. More explicitly, we leverage product graphs to model the high-dimensional graph data and develop multidimensional graph-based vector autoregressive models to forecast future trends with a number of parameters that is independent of the number of time series and a linear computational complexity. Numerical results demonstrating the prediction of moving point clouds corroborate our findings.
Original languageEnglish (US)
Title of host publicationICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages5575-5579
Number of pages5
ISBN (Print)9781509066315
DOIs
StatePublished - Apr 9 2020
Externally publishedYes

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