Particle filtering of ARMA processes of unknown order and parameters

Particle filtering of ARMA processes of unknown order and parameters. Urteaga, I. & Djurić, P. M In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4105-4109, April, 2015.
doi abstract bibtex

This paper considers inference on the widely used state-space models described by hidden ARMA state processes of unknown order observed via non-linear functions of the states. We propose a particle filtering method for sequentially inferring the unknown ARMA time-series by Rao-Blackwellization of all the static unknowns. Our method does not rely either on any assumption on the model order or on the static ARMA and state innovation parameters. Consequently, when the ARMA model order is unknown, it can be used without a follow-up model selection procedure. Extensive simulation results validate the proposed method across different ARMA models.

@InProceedings{ip-Urteaga2015,
  Title                    = {{Particle filtering of {ARMA} processes of unknown order and parameters}},
  Author                   = {I{\~n}igo Urteaga and Petar M Djuri\'{c}},
  Booktitle                = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  Year                     = {2015},
  Month                    = {April},
  Pages                    = {4105-4109},

  Abstract                 = {This paper considers inference on the widely used state-space models described by hidden ARMA state processes of unknown order observed via non-linear functions of the states. We propose a particle filtering method for sequentially inferring the unknown ARMA time-series by Rao-Blackwellization of all the static unknowns. Our method does not rely either on any assumption on the model order or on the static ARMA and state innovation parameters. Consequently, when the ARMA model order is unknown, it can be used without a follow-up model selection procedure. Extensive simulation results validate the proposed method across different ARMA models.},
  Doi                      = {10.1109/ICASSP.2015.7178743},
  Keywords                 = {particle filtering (numerical methods);time series;ARMA time-series;Rao-Blackwellization;hidden ARMA state processes;innovation parameters;nonlinear functions;particle filtering method;state-space models;Computational modeling;Covariance matrices;Estimation;Mathematical model;Noise;State-space methods;Technological innovation;ARMA models;Rao-Blackwellization;State-space models;particle filtering;time-series},
  Owner                    = {iurteaga},
  Timestamp                = {2015-10-15}
}

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