Forecasting Graph Signals with Recursive MIMO Graph Filters

• URL: http://arxiv.org/abs/2210.15258v1
• Date: Thu, 27 Oct 2022 08:25:31 GMT
• Title: Forecasting Graph Signals with Recursive MIMO Graph Filters
• Authors: Jelmer van der Hoeven, Alberto Natali and Geert Leus
• Abstract summary: Forecasting time series on graphs is a fundamental problem in graph signal processing. Existing approaches resort to the use of product graphs to combine this multidimensional information, at the expense of creating a larger graph. In this paper, we show the limitations of such approaches, and propose extensions to tackle them.
• Score: 24.003433487241246
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Forecasting time series on graphs is a fundamental problem in graph signal processing. When each entity of the network carries a vector of values for each time stamp instead of a scalar one, existing approaches resort to the use of product graphs to combine this multidimensional information, at the expense of creating a larger graph. In this paper, we show the limitations of such approaches, and propose extensions to tackle them. Then, we propose a recursive multiple-input multiple-output graph filter which encompasses many already existing models in the literature while being more flexible. Numerical simulations on a real world data set show the effectiveness of the proposed models.

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