Estimize Bull speed using Back propagation
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2014, Vol 4, Issue 11
Abstract
Now knowledge pre-processing, model and reasoning issues, power metrics, quality issues, post-processing of discovered structures, visualization, and on-line change is best challenge. In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the markets[9]. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform “intelligent" tasks similar to those performed by the human brain. Artificial Neural Networks are being counted as the wave of the future in computing. They are indeed self-learning mechanisms which don’t require the traditional skills of a programmer. Back propagation is one of the approaches to implement concept of neural networks. Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most often used as training algorithm in current neural network applications. In this paper, we apply data mining technology to stock market in order to research the trend of price; it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast accuracy
Authors and Affiliations
A. NagaBhushana Rao , K. Eswara Rao
Keywords
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- EP ID EP163156
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