Forecasting Multi-Dimensional Processes over Graphs

• URL: http://arxiv.org/abs/2004.08260v1
• Date: Fri, 17 Apr 2020 14:14:50 GMT
• Title: Forecasting Multi-Dimensional Processes over Graphs
• Authors: Alberto Natali, Elvin Isufi, Geert Leus
• Abstract summary: 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. Numerical results demonstrating the prediction of moving point clouds corroborate our findings.
• Score: 34.23046028631646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 multi-dimensional 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.

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