Time-varying ARMA stable process estimation using sequential Monte Carlo

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.