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Prediction of stock index remains a challenging task of the financial time series prediction process. Though different non-linear prediction models are in use, their prediction accuracy does not improve beyond certain level. In order to improve the forecasting accuracy, this paper proposes a chemical reaction optimization based virtual data position (CROVDP) exploration method for financial time series prediction. From the given data points in the training set, CROVDPs are explored by the proposed method and incorporated to the original financial time series to enhance the prediction accuracy of a multilayer perceptron (MLP) based forecasting model. The effectiveness of the proposed method is compared with three deterministic methods such as linear, Lagrange and Taylor interpolation as well as two stochastic methods such as uniform and Gaussian method. To train and validate the models in presence of virtual data positions (VDP), ten real stock indices are used. Simulation results show that the models developed with the proposed CROVDP exploration method yields much better prediction accuracy than others.

Artificial chemical reaction optimization based neural net for virtual data position exploration for efficient financial time series forecasting