Forecasting by Using the Optimal Time Series Method

Abstract

The research objective is to discuss the adoption of the wavelet transformation method (WT) in processing time series, for its efficiency. As well as comparing modern methods represented by wavelet and neural networks with traditional methods represented by Box-Jenkins models, to better diagnose the treatment of any series, whether (linear, semi-linear or non-linear), a way to minimize the error to the nearest zero value, through the use of error accuracy measurements to diagnose the best method among classical and modern methods, some of them are characterized by artificial intelligence (IT) to measuring the accuracy of best forecasting methods in time series. Artificial neural networks (ANN) also used as one of the uses of artificial intelligence (IT) for best results. statistical error criteria have been adopted for comparing and evaluating the efficiency of the methods adopted: (MSE, RMSE, MAPE), Mat lab 8th edition was used. An important conclusion reached, was finding the best technique that minimizes the error to its lowest value with an average error close to zero significantly.