Stock Price Prediction Using Deep Learning Model

Abstract

Prediction and the analysis of data obtained from the stock market both plays a significant part in the economy of today. The neural network is one of the sophisticated data mining techniques that has been employed by academics in a variety of fields over the last several decades. These researchers have been looking into a wide range of topics. Linear models and non-linear models are two classifications that may be used to the many different methods that are employed in forecasting. Deep LSTM underpinned the stock market prediction model. This model accurately predicts the stock market. This research yielded two stock market forecasting improvements. This scenario modifies the deep LSTM classifier to predict stock market. The Time series approach is used to train the deep LSTM, which approximates hyperparameters using step size and sparse gradient factor, that provide significant improvement in the prediction.