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The aim of this paper is to present a method for neural network input   parameters selection and preprocessing. The purpose of this network is to   forecast foreign exchange rates using artificial intelligence. Two data sets   are formed for two different economic systems. Each system is represented by   six categories with 70 economic parameters which are used in the analysis.   Reduction of these parameters within each category was performed by using the   principal component analysis method. Component interdependencies are   established and relations between them are formed. Newly formed relations   were used to create input vectors of a neural network. The multilayer feed   forward neural network is formed and trained using batch training. Finally,   simulation results are presented and it is concluded that input data   preparation method is an effective way for preprocessing neural network data.   [Projekat Ministarstva nauke Republike Srbije, br.TR 35005, br. III 43007 i   br. III 44006

Input data preprocessing method for exchange rate forecasting via neural network