Optimum ARX Model Prediction for Monthly Air Temperature Changes in Delta, Egypt

Abstract

This study aims to study the ability application of nonlinear Auto-Regression model with exogeneous inputs (ARX) in forecasting time series monthly temperatures changes in Delta, Egypt for 49 years (1960 to 2009) monitoring data. Three methods are used to estimate the optimal parameters of ARX model identification which are the normalized Least Mean Square (LMS), artificial Neural Network (NN) and Wavenet Neural network (WN). The time series temperature changes from 18 weather stations in Delta are used to compare and estimate the best method for the temperature change models. The models results indicate that the worst case solution for ARX model is LMS while the WN is found to be better than NN in the training period. The NN is found an acceptable performance for training and testing periods. The 95% auto-correlation function for the residuals models shows that there is no loss of information is observed for the applied ARXNN model; however, the ARXNN technique can be successfully used to predict the monthly temperatures of any site at the Delta area in Egypt.