Forecasting Multi-Dimensional Processes Over Graphs

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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
Title of host publicationICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Subtitle of host publicationProceedings
PublisherIEEE
Pages5575-5579
Number of pages5
ISBN (Electronic)978-1-5090-6631-5
ISBN (Print)978-1-5090-6632-2
DOIs
Publication statusPublished - 2020
EventICASSP 2020: IEEE International Conference on Acoustics, Speech and Signal Processing - Barcelona, Spain
Duration: 4 May 20208 May 2020

Conference

ConferenceICASSP 2020
CountrySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • Forecasting
  • graph signal processing
  • product graphs
  • time series
  • vector autoregressive model

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  • Cite this

    Natali, A., Isufi, E., & Leus, G. (2020). Forecasting Multi-Dimensional Processes Over Graphs. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings (pp. 5575-5579). IEEE. https://doi.org/10.1109/ICASSP40776.2020.9053522