Document - Time-varying ARMA stable process estimation using sequential Monte Carlo

2013

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Time-varying ARMA stable process estimation using sequential Monte Carlo

Huang R., Zheng H., Kuruoglu E. E.

Electrical and Electronic Engineering Alpha-stable process Sequential Monte Carlo Particle filtering Signal Processing

Various time series data in applications ranging from telecommunications to financial analysis and from geophysical signals to biological signals exhibit non-stationary and non-Gaussian characteristics. ?-Stable distributions have been popular models for data with impulsive and nonsymmetric characteristics. In this work, we present timevarying autoregressive moving-average ?-stable processes as a potential model for a wide range of data, and we propose a method for tracking the time-varying parameters of the processwith ?-stable distribution. The technique is based on sequential Monte Carlo, which has assumed a wide popularity in various applications where the data or the system is non-stationary and non-Gaussian.

Source: Signal, image and video processing (Print) 7 (2013): 951–958. doi:10.1007/s11760-011-0285-x

Publisher: Springer, London , Regno Unito

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@article{oai:it.cnr:prodotti:276882,
	title = {Time-varying ARMA stable process estimation using sequential Monte Carlo},
	author = {Huang R. and Zheng H. and Kuruoglu E. E.},
	publisher = {Springer, London , Regno Unito},
	doi = {10.1007/s11760-011-0285-x},
	journal = {Signal, image and video processing (Print)},
	volume = {7},
	pages = {951–958},
	year = {2013}
}