EFFECT OF LINEAR DATA PROCESSING PROCESSES ON THE PREDICTION PERFORMANCE OF THE NEURAL NETWORK: AN APPLICATION WITH EXCHANGE RATE DATA

Abstract

This study investigated the effect of traditional data preprocessing processes on the prediction performance of predictions made with artificial neural network models. For this purpose, two different models were created and time series estimation was made. The original data was used in the first model, and in the second model, the data obtained by the traditional data preprocessing method in the time series were used. The data set consists of monthly real US Dollar/Turkish Lira rates between 2000M1 and 2022M2 for Turkey. Jordan model with feedback artificial neural network architecture is used for time series estimation. Estimation errors were calculated according to the Root Squared Value of Mean Squared Error (RMSE) criteria and the results were discussed according to this statistic. In the study, it was concluded that data processing reduces the estimation error of the nonlinear method.