Stock Market Indices Prediction Using Time Series Analysis

In this paper we present two non-parametric approaches used for time series analysis and modeling for a financial time series: the DJIA stock index open values. We used two recently developed algorithms and methods for time series prediction, Gene Expression Programming and Neural Networks because they are suitable for the series that present high variability, as in the present situation. After using these approaches we managed to obtain models which explain 92% of the variance in the case of GEP and 98% in the case of Multilayer Perceptron. Key-Words: Gene Expression Programming, Neural networks, time series, DJIA, evaluation, accuracy