TY - GEN
T1 - Sequential Monte Carlo sampling for systems with fractional Gaussian processes
AU - Urteaga, Inigo
AU - Bugallo, Monica F.
AU - Djuric, Petar M.
N1 - Publisher Copyright:
© 2015 EURASIP.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - In the past decades, Sequential Monte Carlo (SMC) sampling has proven to be a method of choice in many applications where the dynamics of the studied system are described by nonlinear equations and/or non-Gaussian noises. In this paper, we study the application of SMC sampling to nonlinear state-space models where the state is a fractional Gaussian process. These processes are characterized by long-memory properties (i.e., long-range dependence) and are observed in many fields including physics, hydrology and econometrics. We propose an SMC method for tracking the dynamic longmemory latent states, accompanied by a model selection procedure when the Hurst parameter is unknown. We demonstrate the performance of the proposed approach on simulated time-series with nonlinear observations.
AB - In the past decades, Sequential Monte Carlo (SMC) sampling has proven to be a method of choice in many applications where the dynamics of the studied system are described by nonlinear equations and/or non-Gaussian noises. In this paper, we study the application of SMC sampling to nonlinear state-space models where the state is a fractional Gaussian process. These processes are characterized by long-memory properties (i.e., long-range dependence) and are observed in many fields including physics, hydrology and econometrics. We propose an SMC method for tracking the dynamic longmemory latent states, accompanied by a model selection procedure when the Hurst parameter is unknown. We demonstrate the performance of the proposed approach on simulated time-series with nonlinear observations.
KW - Sequential Monte Carlo
KW - fractional Gaussian process
KW - particle filtering
KW - state-space models
UR - https://www.scopus.com/pages/publications/84963979631
U2 - 10.1109/EUSIPCO.2015.7362583
DO - 10.1109/EUSIPCO.2015.7362583
M3 - Conference contribution
AN - SCOPUS:84963979631
T3 - 2015 23rd European Signal Processing Conference, EUSIPCO 2015
SP - 1246
EP - 1250
BT - 2015 23rd European Signal Processing Conference, EUSIPCO 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd European Signal Processing Conference, EUSIPCO 2015
Y2 - 31 August 2015 through 4 September 2015
ER -