KAUST Grant NumberOSR-2015-Sensors-2700
Preprint Posting Date2020-04-17
Online Publication Date2020-04-09
Print Publication Date2020-05
Permanent link to this recordhttp://hdl.handle.net/10754/665249
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AbstractThe 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.
CitationNatali, A., Isufi, E., & Leus, G. (2020). Forecasting Multi-Dimensional Processes Over Graphs. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp40776.2020.9053522
SponsorsThis work was supported in parts by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.
Conference/Event name2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020