Open AccessStock Prediction using Neural Networks and Time Series Analysis Methods
Author(s) -
Mohammad Pardaz Banu
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit206495
Subject(s) - stock market , artificial neural network , stock (firearms) , time series , stock market prediction , econometrics , stock price , stock market bubble , computer science , series (stratigraphy) , financial economics , economics , machine learning , engineering , mechanical engineering , paleontology , horse , biology
The stock market is considered to be one of the most highly complex financial systems which consist of various components or stocks, the price of which fluctuates greatly with respect to time. Stock market forecasting involves uncovering the market trends with respect to time. All the stock market investors aim to maximize the returns over their investments and minimize the risks associated. There are time series methods such as AR, MA, SARIMAX developed to predict the stock price but neural network methods such as CNN, LSTM also used to predict the stock price. This research paper describes the prediction of stock market using neural network alogorithms and also few time series methods.