Business Predictions through Artificial Neural Networks
Sonal Saurabh1, Ruchi Sehrawat2
1Ms.Sonal Saurabh, Student, M. Tech, Information Technology, University School Of Information, Communication & Technology(GGSIPU), New Delhi, India.
2Mrs. Ruchi Sehrawat , Assistant Professor, University School of Information, Communication & Technology, GGSIPU, New Delhi, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 30, 2020. | Manuscript published on May 10, 2020. | PP: 746-750 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5451059720/2020©BEIESP | DOI: 10.35940/ijitee.G5451.059720
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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: In this paper a prediction encompassing one of the applications of Neural Networks has been presented. With the advancements in neural networks, it holds the capability to envisage stock movement with high precision. With the progress in time, widespread commercial applications gained much importance through the use of Artificial neural networks The goal encompassing this work is to provide an insight of stock market prediction system entailing a methodical outline of neural network, back-propagation and various hybrid network serving the purpose of performance enhancement. According to the current situation, designing of unique system pertinent to optimized prediction is required for decision making by many organizations. Neural networks assimilates further scope of investigation and upsurge the knowledge of artificial neural networks in various domains. In this work, neural networks along with principal component analysis has been discussed. The software which has been considered for achieving the results is Weka. This paper also covers the challenges and further scope of research for a better prediction.
Keywords: Artificial neural network, back-propagation, Component Analysis, hybridization, hyper-parameters, PCA.
Scope of the Article: Artificial Neural Networks