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Stock Price Prediction using Mean Normalization and Neural Networks


Rohit Kriplani , VIT UNIVERSITY; Prof. Sankar Ganesh, VIT University


Normalization, Backpropagation algorithm, error computation


This paper proposes neural networks a non-linear approach to predict future trends of stock market. The model is made up of neural networks whose data are pre-processed using mean normalization technique scaled between -1 to 1. The model is created on MATLAB in the form of simulation work. The method used here is multilayer perceptron taking different number of hidden nodes into account. The Reliance stocks is considered. The sum of squares of differences between target and predicted errors is computed with 4-3-1, 4-5-1 and 4-7-1 neural network architecture.

Other Details

Paper ID: IJSRDV3I2649
Published in: Volume : 3, Issue : 2
Publication Date: 01/05/2015
Page(s): 994-997

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