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.