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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
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Published Paper Details:
Paper Title
LSTM AND REGRESSION METHODS FOR STOCK MARKET PREDICTION
Authors
Nishanth Vaidya, NIkhil Bharadwaj
Keywords
Keywords - Least Square Support Vector Machine, Particle Swarm Optimization, regression, and volume.
Abstract
By using stock market prediction, the aim is to predict the values of stocks in the future. There has been a growing interest in stock market prediction technologies via the use of machine learning. This is done by taking the current values of the market after taking previous stock values as the training data. Various Machine learning techniques can be used to make prediction simpler in general. The proposed paper works on the working of 2 different algorithms, the Regression and LSVM based algorithm.
IJCRT's Publication Details
Unique Identification Number - IJCRT2005021
Paper ID - 194230
Page Number(s) - 139-142
Pubished in - Volume 8 | Issue 5 | May 2020
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Nishanth Vaidya, NIkhil Bharadwaj, "LSTM AND REGRESSION METHODS FOR STOCK MARKET PREDICTION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.8, Issue 5, pp.139-142, May 2020, Available at :http://www.ijcrt.org/papers/IJCRT2005021.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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