Separable Autoregressive Moving Average Graph-Temporal Filters

E. Isufi, A. Loukas, A. Simonetto, G. Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Despite their widespread use for the analysis of graph data, current graph filters are designed for graph signals that do not change over time, and thus they cannot simultaneously process time and graph frequency content in an adequate manner. This work presents ARMA2D, an autoregressive moving average graph-temporal filter that captures jointly the signal variations over the graph and time. By its unique nature, this filter is able to achieve a separable 2-dimensional frequency response, making it possible to approximate the filtering specifications along both the graph and temporal frequency domains. Numerical results show that the proposed solution outperforms the state of the art graph filters when the graph signal is time-varying.
Original languageEnglish
Title of host publication2016 24th European Signal Processing Conference (EUSIPCO)
Place of PublicationPiscataway
Number of pages5
ISBN (Electronic)978-0-9928-6265-7
ISBN (Print)978-1-5090-1891-8
Publication statusPublished - 1 Dec 2016
EventEUSIPCO 2016: 24th European Signal Processing Conference - Budapest, Hungary
Duration: 29 Aug 20162 Sept 2016
Conference number: 24


ConferenceEUSIPCO 2016
Abbreviated titleEUSIPCO
Internet address


  • signal processing over graphs
  • graph filters
  • separable graph-temporal filters
  • distributed signal processing


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