Stock Market Predictor using Long short-term Memory (LSTM) Technique
Ashish Virendra Chandak
Ashish Virendra Chandak, Department of Information Technology, Shri Ram deobaba College of Engineering and Management, Nagpur, Maharashtra. India
Manuscript received on January 12, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 393-396 | Volume-9 Issue-4, February 2020. | Retrieval Number: D9075019420/2020©BEIESP | DOI: 10.35940/ijitee.D9075.029420
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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 the stock market, it is important to have accurate prediction of future behavior of stock price. Because of the great chance of financial loss as well as scoring profits at the same time, it is mandatory to have a secure prediction of the values of the stocks. But when it comes to predicting the value of a stock in future we tend to follow stock market experts but as technology is progressing we may use these technologies rather than following human experts who may be biased many times. Stock price prediction has been interesting area for investors and researchers. This article proposes an approach towards prediction of stock price using machine learning model Long Short Term Memory. This is an ensemble learning method that has been an exceedingly successful model for predicting sequence of numbers and words. Long Short Term Memory is a machine learning model for prediction. This technique is used to forecast the future stock price of a specific stock by using historical data of the stock gathered from Yahoo! Finance.
Keywords: LSTM, Stock price prediction
Scope of the Article: Software Engineering Techniques and Production Perspectives