Stock prediction and analysis using intermittent training data with artificial neural networks

Forecasting stock market using neural networks has various tasks and data mining techniques that has no conventional formulation of methods. It has no specified optimal neural analysis that has efficient application that foretells the stock market values. According to the views and status, its history of stock based on the previous values its rises and downfalls etc has some trouble in finding out the anecdotal market values. In our paper we propose efficient algorithm for predicting the stock based values that use artificial intelligence in all aspects. We inculcate several layer of insight over the previous stock market prices by processing the training data values and used them inside various layers and obtain results. It also intermittently send training data thus to get appropriate stock values and accurate forecasting. This combined method of forecasting using various layers and intermittent training data obtainment helps in getting accurate results based on current scenario of stock market values.

I. Introduction

The common goals of human tendency are to lead a very simple and less risk taking life. For every goal we need hacks and loopholes to attain the expected results and outcomes. Even in the field of stock market which produces most varying results that could not even find by more technical, statistical, basic indices model etc. As the market all over the world is changing consistently there is no clue for its estimating its anecdotal values. Along with the development in artificial neural networks stock market forecasting techniques are emphasized. Over the years the artificial intelligence networks have successfully reaches development in all the fields. Especially in the field of neural network the processing system has operations parallel to the functionalities determined by network of neural structure.