Analysis of financial data on the time series using data from the stock market

Abstract

Predicting financial data is really important for investors Often times investors do not have a proper tool to properly assess the market and forecast their predictions. Furthermore, not only investors in modern day civilians are also willing to invest as well and as there is an abundant amount of data available from the financial sector it is of utmost significance to find the optimal algorithm in a general case scenario. This project aims to show a comparison between the results found from some of the popular neural network algorithms. In this project we have employed the help of Dense Neural Network [DNN], Recurrent Neural Network [RNN], Long Short Term Memory unit [LSTM], Convolutional Neural Network [CNN] and a pipeline where we combined LSTM and CNN. We have kept some of the parameters similar and compared the results to determine an algorithm in a general case. This would help people take informed decisions while investing.