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تØديد نماذج المتسلسلات الزمنية الدورية ذاتية الانØدار متوسطات المتØركة باستخدام R
Periodic autoregressive moving average PARMA process extend the classical autoregressive moving
average ARMA process by allowing the parameters to vary with seasons. Model identification is the
identification of a possible model based on an available realization, i.e., determining the type of the
model with appropriate orders. The Periodic Autocorrelation Function (PeACF) and the Periodic
Partial Autocorrelation Function (PePACF) serve as useful indicators of the correlation or of the
dependence between the values of the series so that they play an important role in model identification.
The identification is based on the cut-off property of the Periodic Autocorrelation Function (PeACF).
We derive an explicit expression for the asymptotic variance of the sample PeACF to be used in
establishing its bands. Therefore, we will get in this study a new structure of the periodic
autocorrelation function which depends directly to the variance that will derived to be used in
establishing its bands for the PMA process over the cut-off region and we have studied the theoretical side
and we will apply some simulated examples with R which agrees well with the theoretical results.تØديد نماذج المتسلسلات الزمنية الدورية ذاتية الانØدار متوسطات المتØركة باستخدام