استعمال أسلوب الشبكات العصبية الاصطناعية و طريقة الإمكان الأعظم التقريبية في تشخيص و تقدير افضل أنموذج موسمي لدرجات الحرارة في محافظة بابل

الملخص EN

In this research, the series of daily temperatures recorded in the Babylon Governorate for the year (2020), which included (334) observations, which were obtained from the Iraqi Meteorological Authority, was analyzed using autoregressive seasonal moving averages SARIMA models and autoregressive seasonal multiple moving averages models depending on programs (Minitab 17, Matlab 2015, E-views 10) with the aim of diagnosing the best seasonal model for the time series from a set of models that have been reconciled, and then choosing the best model from these models using the comparison criteria between models (AIC, BIC, SMAIC) and then estimating The diagnosed model using the approximate maximum potential method, and then comparing this method with the artificial neural networks NARX method.

It was concluded that the doubled seasonal model ARIMA(p, q, d)x(P, Q, D)2 as a representative model for the seasonal time series under study, as well as the multiple seasonal model of the rank ARIMA(1, 1, 0)x(1, 2, 0)2 as the best seasonal model for the temperature series.

It was found that the artificial neural network NARX method is better than the approximate maximum likelihood method based on the MSE standard.