Properties And Experimental Of Gaussian And Non Gaussian Time Series Model

Most of time series that appear in many economical geophysical and other phenomena are driven by nonGaussian white noise ( ), in this paper investigate some probabilistic properties of Gaussian and nonGaussian mixed with identification methods of ARMA model. We have theoretically derived the characteristic function the first of (four moments) of skeweness and kurtosis coefficients for white noise ( ) with Gaussian and nonGaussian (Poisson) distribution, simulation experiments were used to confirm the accuracy of the theoretical results. Declared the identification sample Autocorrelation function (ESACF) and (Kumar) method (Ctable) which depending upon the pad approximation and suggested new method depending upon the extended sample partial Autocorrelation function (ESPACF) and find ascertain efficiency of suggested method.