
Stock Market Prediction Using LSTM
Author(s) -
Ishwarappa Kalbandi,
Ashutosh Jare,
Om Kale,
Himanshu Borole,
Swapnil Navsare
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-877
Subject(s) - stock market prediction , computer science , stock market , artificial neural network , stock (firearms) , deep learning , stock market index , encoder , artificial intelligence , recurrent neural network , machine learning , econometrics , economics , mechanical engineering , paleontology , horse , engineering , biology , operating system
This paper aims to develop an innovative neural network approach to achieve better stock market predictions. Data were obtained from the live stock market for real-time and off-line analysis and results of visualizations and analytics to demonstrate Internet of Multimedia of Things for stock analysis. To study the influence of market characteristics on stock prices, traditional neural network algorithms may incorrectly predict the stock market, since the initial weight of the random selection problem can be easily prone to incorrect predictions. Based on the development of word vector in deep learning, we demonstrate the concept of “stock vector.” The input is no longer a single index or single stock index, but multi-stock high-dimensional historical data. We propose the deep long short-term memory neural network (LSTM) with embedded layer and the long short-term memory neural network with automatic encoder to predict the stock market. In these two models, we use the embedded layer and the automatic encoder, respectively, to vectorize the data, in a bid to forecast the stock via long short-term memory neural network. The experimental results show that the deep LSTM with embedded layer is better.