Escalation of Prediction Accuracy With Virtual Data

Abstract

Random fluctuations occur in the trend of financial time series due to many macroeconomic factors. Such fluctuations lead to sudden fall after a constant raise or a sudden rise after a constant fall, which are difficult to predict from previous data points. At the fluctuation point, previous data points that are not too close to the target price adversely influence the prediction trend. Far away points may be ignored and close enough virtual data points are explored and incorporated in order to diminish the adverse prediction trend at fluctuations. From the given data points in the training set, virtual data positions (VDP) can be explored and used to enhance the prediction accuracy. This chapter presents some deterministic and stochastic approaches to explore such VDPs. From the given data points in the training set, VDPs are explored and incorporated to the original financial time series to enhance the prediction accuracy of the model. To train and validate the models, ten real stock indices are used and the models developed with the VDPs yields much better prediction accuracy.