Open AccessStock Prices Prediction with Recurrent Neural Networks
Author(s) -
Middi Appala Raju,
Venkata Sai Rishita Middi
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f3308.049620
Subject(s) - artificial neural network , stock (firearms) , architecture , computer science , stock market , recurrent neural network , stock market prediction , stock price , long short term memory , capital investment , artificial intelligence , financial economics , economics , finance , series (stratigraphy) , engineering , mechanical engineering , art , paleontology , horse , visual arts , biology
Data and Information is the base for making investment choices. Stock market is typically a place where shares of certain companies trying to raise their capital, are traded. With the availability of large amount of data and refinement methods, investors nowadays, are able to make rational investment decisions. Advancement in computational intelligence, use of AI in the form of Neural Networks has created a new basis for predicting stock prices. In this work, we have employed Recurrent Neural Networks to implement time series prediction. The Long Term Short Memory Architecture has been used as the network architecture to perform prediction on Apple Stock Prices. The implementation is done on Keras platform.