PLS COMPUTATIONAL MODEL FOR PREDICTION IN TIME SERIES

Some data fluctuates rapidly in a short period of time. Classical and computational models are useful in predicting thishighly volatile data. In this study Partial Least Square Regression and computational neural network models are used to explorestock market tendency. Thirteen variables are considered to predict the daily closing prices of BSE sensex data. To evaluate theprediction ability of the models, standard error values are calculated. The results revealed that Nonparametric PLS regressionmodel is better in prediction.