www.ijstr.org Properties And Experimental Of Gaussian And Non Gaussian Time Series Model

Abstract: Most of time series that appear in many economical geophysical and other phenomena are driven by non- Gaussian white noise ( ), in this paper investigate some probabilistic properties of Gaussian and non- Gaussian 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 non-Gaussian (Poisson) distribution, simulation experiments were used to confirm the accuracy of the theoretical results. Declared the identification sample Autocorrelation function (ESACF) and (Kumar) method (C- table) 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. Index Terms: Gaussian white noise distribution, non- Gaussian white noise distribution, ARMA process, characteristic function of moments fo