Share Market Prediction Using Long Short Term Memory and Artificial Neural Network

Abstract

This paper explains prediction of share market trends of organizations using Artificial Neural Network (ANN). The Long Short Term Memory (LSTM) incorporated with a simple neural network gives the result of the movement of company's stock prices in the share market. LSTM is used for processing the time-series data. LSTM is a type of Recurrent Neural Network (RNN). In this work, layers of LSTM networks called stacked LSTM is a core component that process the huge volume of time series data. LSTM model works like a human brain because of the power to have a short term and long term memory. During data processing in the training stage, the model keeps a short term memory of the relation between the date and stock prices which is available in the data. It then starts keeping track of the relations from the successive dates and stock prices since the inception of the company. In this stage, the model tries to find a pattern or a trend in the stock price movement. This is kept in the long term memory. As the model processes further data, it finds an accurate pattern in the stock price movement. The exact date or a number of days is given as input and the stock price is given as output from the model