Stock Price Prediction Using Stacked LSTM

Stock values is very valuable but extremely hard to predict correctly for any human being on their own. This project seeks to solve the problem of Stock Prices Prediction by utilizes Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict future stock values. Historical data about the stock values that have been publicly listed by Quandl has been used in this project and I have used the stock value data of ‘Tata Global Beverages’. This can be considered as a Time series analysis is a specialized branch of statistics used extensively in fields such as Econometrics & Operation Research. This is specifically designed time series problem for you and the challenge is to forecast traffic.

In the past decades, there is an increasing interest in predicting markets among economists, policymakers, academics and market makers. The objective of the proposed work is to study and improve the supervised learning algorithms to predict the stock price.

The technical objectives will be implemented in Python. The system must be able to access a list of historical prices. It must calculate the estimated price of stock based on the historical data for the next 30 days. It must also provide an instantaneous visualization of the market index in a neatly formatted Python-Based Web App.