The effects of pre-processing methods on forecasting improvement of Artificial Neural Networks

Along other methods, Intelligent Methods can be used in order to model the trend of changes of a certain variable. These methods require data to be preprocessed before being used in the forecasting process. Generally, the preprocessing step includes omitting outliers, assessment of the missing data, data smoothing, etc. In this paper, the effect of various smoothing methods on the final forecasted results is studied. Furthermore, data from the electricity consumption in Iran over the past 20 years were used as actual data. After being smoothed, these data are then incorporated into an Artificial Neural Network in order to forecast the electrical consumption. The comparisons between several Seasonal Decomposition, including Seasonal Adjustment Series (SAS) and Seasonal Trend Cycle (STC), Exponential Smoothing (Simple, Linear, Holt and Winter) and Box- Jenkins (Moving Average, Auto Regression, and Auto Regression Integrated Moving Average) methods show the superiority of SAS in Decomposition categorization over other methods. The structure of this study may be used for other data sets for improvement of data pre-processing.