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 this<br>highly volatile data. In this study Partial Least Square Regression and computational neural network models are used to explore<br>stock market tendency. Thirteen variables are considered to predict the daily closing prices of BSE sensex data. To evaluate the<br>prediction ability of the models, standard error values are calculated. The results revealed that Nonparametric PLS regression<br>model is better in prediction.