Influence of data orthogonality: on the accuracy and stability of financial market predictions

Abstract

Input selection is always important for adapting artificial intelligence systems for forecasting. Recurrent neural networks could predict using the historical data of financial markets but the predictions are very unstable. The goal of our paper is to study the influence of two historical data inputs on accuracy and stability of recurrent neural network forecasting. It is proposed to use orthogonal recurrent neural network inputs for the prediction of financial market exchange rates. Statistical comparison of the predicted results for different degrees of orthogonality of the data inputs shows much tighter distribution of the predicted results, when the more orthogonal input data are used. This proposed data input concept was tested using evolution of recurrent systems with linear Outputs recurrent neural network with historical input data of currency exchange rates.