Predict the values of the time series using the (ARMAX) model with practical application


Researchers have great interest in studying the black box models this thesis has been focused in the study one of the black box models , a ARMAX model which is one of the important models and can be accessed through a number of special cases which models (AR , MA , ARMA, ARX) , which combines method of the time series that depend on historical data and and regression method as explanatory variables addition to that past errors , ARMAX model importance has appeared in many areas of application that direct contact with our daily lives , it consists of constructing ARMAX model several traditional stages of the process , a identification As it was used Final prediction error (FPE) , Akaiki Information Criterion (AIC) and estimate As it was used Recursive least square with Forgetting Factor (RLS – F) and Recursive pseudolinear regression method (RPLR) which come in the first place and (RLS – F) which come in the second place and finally come prediction for (30) value of the daily maximum temperature depending on the daily wind speed