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Forecasting Multi-Dimensional Processes Over Graphs
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
doi:10.1109/icassp40776.2020.9053522
dblp:conf/icassp/NataliIL20
fatcat:gfyrtkqhvjedtesbbrghtqopqy