Stock Prices Prediction with Recurrent Neural Networks
Middi Appala Raju1, Venkata Sai Rishita Middi2
1Middi Appala Raju, Department of Management Studies, Christ(Deemed to be University), Bangalore, India.
2Venkata Sai Rishita Middi, Department of Electrical and computer Engineering, Brown University, Providence, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on April 10, 2020. | PP: 630-632 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3308049620/2020©BEIESP | DOI: 10.35940/ijitee.F3308.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Activation, Neural Networks prices,prediction, Stocks Regressor, Testing, Performance
Scope of the Article: Sensor Networks, Actuators for Internet of Things