Optimizing Stock market prediction using Long Short Term Memory
R. Kavitha1, Sonal Singh2
1R.Kavitha, Department of CSE, Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology.
2Sonal Singh, Student, Department of CSE, Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology.
Manuscript received on October 13, 2019. | Revised Manuscript received on 24 October, 2019. | Manuscript published on November 10, 2019. | PP: 2819-2823 | Volume-9 Issue-1, November 2019. | Retrieval Number: J98240881019/2019©BEIESP | DOI: 10.35940/ijitee.J9824.119119
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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: Stock market prediction has been an important issue in the field of finance, engineering and mathematics due to its potential financial gain. Stock market prediction is a process of predicting the future value of a company stock or other financial instrument traded in financial market. Stock market prediction brings with it the challenge of proving whether the financial market is predictable or not, since stock market data is of high velocity. This project proposes a machine learning model to predict stock market price based on the data set available by using LSTM model for performing prediction by de-noising the data using wavelet transform and performing auto-encoding on the data. The process includes removal of noise, preprocessing, feature selection, data mining, analysis and derivations. This project focuses mainly on the use of LSTM algorithm along with a layer of neural network to forecast stock prices and allocate stocks to maximize the profit within the risk factor range of the stock buyers and sellers.
Keywords: Prediction, LSTM, De-noise, Auto Encoders, Feature Selection
Scope of the Article: Reasoning and Inference Marketing and Social Sciences