Autoregressive Moving Average Graph Filtering

Elvin Isufi, Andreas Loukas, Andrea Simonetto, Geert Leus

Research output: Contribution to journalArticleScientificpeer-review

175 Citations (Scopus)


One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogs of classical filters, but intended for signals defined on graphs. This paper brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which are able to approximate any desired graph frequency response, and give exact solutions for specific graph signal denoising and interpolation problems. The philosophy to design the ARMA coefficients independently from the underlying graph renders the ARMA graph filters suitable in static and, particularly, time-varying settings. The latter occur when the graph signal and/or graph topology are changing over time. We show that in case of a time-varying graph signal, our approach extends naturally to a two-dimensional filter, operating concurrently in the graph and regular time domain. We also derive the graph filter behavior, as well as sufficient conditions for filter stability when the graph and signal are time varying. The analytical and numerical results presented in this paper illustrate that ARMA graph filters are practically appealing for static and time-varying settings, as predicted by theoretical derivations.

Original languageEnglish
Article number7581108
Pages (from-to)274-288
Number of pages15
JournalIEEE Transactions on Signal Processing
Issue number2
Publication statusPublished - 2017


  • autoregressive moving average graph filters
  • Distributed graph filtering
  • infinite impulse response graph filters
  • signal processing on graphs
  • time-varying graph signals
  • time-varying graphs


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