Forecasting Multi-Dimensional Processes Over Graphs

Alberto Natali, Elvin Isufi, Geert Leus
2020 ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
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
more » ... h 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.
doi:10.1109/icassp40776.2020.9053522 dblp:conf/icassp/NataliIL20 fatcat:gfyrtkqhvjedtesbbrghtqopqy