Distributed autoregressive moving average graph filters

Andreas Loukas, Andrea Simonetto, Geert Leus

Research output: Contribution to journalArticleScientificpeer-review

77 Citations (Scopus)


We introduce the concept of autoregressive moving average (ARMA) filters on a graph and show how they can be implemented in a distributed fashion. Our graph filter design philosophy is independent of the particular graph, meaning that the filter coefficients are derived irrespective of the graph. In contrast to finite-impulse response (FIR) graph filters, ARMA graph filters are robust against changes in the signal and/or graph. In addition, when time-varying signals are considered, we prove that the proposed graph filters behave as ARMA filters in the graph domain and, depending on the implementation, as first or higher order ARMA filters in the time domain.
Original languageEnglish
Pages (from-to)1931-1935
Number of pages5
JournalIEEE Signal Processing Letters
Issue number11
Publication statusPublished - 2015


  • Distributed time-varying computations
  • graph filters
  • graph Fourier transform
  • signal processing on graphs


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