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  Published Paper Details:

  Paper Title

LSTM BASED STOCK PRICE PREDICTION

  Authors

  Pritam Ahire,  Hanikumar Lad,  Smit Parekh,  Saurabh Kabrawala

  Keywords

Long Short Term Memory, Recurrent Neural Network, Machine learning, Stock Price Prediction

  Abstract


Stock market investment is one of the most complex and sophisticated way to do business. Stock market is very uncertain as the prices of stocks keep fluctuating because of several factors that makes prediction of stocks a difficult and extremely complicated task. Nowadays investors need fast and accurate information to make effective decisions and highly interested in the research area with exponentially growing technological advances of stock price prediction. Understanding the pattern of stock price of a particular company by predicting their future development and financial growth will be highly beneficial. This paper focuses on the usage of a type of recurrent neural network (RNN) based Machine learning which is known as Long Short Term Memory (LSTM) to predict stock values.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2102617

  Paper ID - 203894

  Page Number(s) - 5118-5122

  Pubished in - Volume 9 | Issue 2 | February 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Pritam Ahire,  Hanikumar Lad,  Smit Parekh,  Saurabh Kabrawala,   "LSTM BASED STOCK PRICE PREDICTION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 2, pp.5118-5122, February 2021, Available at :http://www.ijcrt.org/papers/IJCRT2102617.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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