DATA PREPROCESSING METHODS FOR INTERVAL BASED NEURAL NETWORK PREDICTION

Aleksejs Zorins

Abstract


The paper examines a task of forecasting stock prices of Riga Stock exchange by the use of interval value prediction approach, which is carried out by modified Kohonen neural network learning algorithm. The data preprocessing methods are analyzed and implemented here to solve stock prices prediction task. The proposed data preprocessing methods has been experimentally tested with two types of artificial neural networks.


Keywords


artificial neural networks; interval value prediction; Kohonen neural networks; time series transformation methods

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References


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DOI: http://dx.doi.org/10.17770/etr2007vol1.1746

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