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<article xlink="http://www.w3.org/1999/xlink" mml="http://www.w3.org/1998/Math/MathML" xsi="http://www.w3.org/2001/XMLSchema-instance" ali="http://www.niso.org/schemas/ali/1.0/" noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" article-type="research-article" dtd-version="1.1" lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">isrdo-SRJSET</journal-id><journal-id journal-id-type="pmc">isrdo-SRJSET</journal-id><journal-id journal-id-type="nlm-ta">isrdo-SRJSET</journal-id><journal-title-group><journal-title>Scientific Research Journal of Science, Engineering and Technology</journal-title><abbrev-journal-title abbrev-type="publisher" pub-type="epub">SRJSET</abbrev-journal-title></journal-title-group><issn>2584-0584</issn><publisher><publisher-name>ISRDO</publisher-name><publisher-loc>Gujarat,India</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">M-10035</article-id><article-id pub-id-type="doi"/><article-categories><subj-group subj-group-type="categories"><subject>Mathematics</subject></subj-group></article-categories><title-group><article-title>MATHEMATICAL MODEL FOR STOCK PRICE PREDICTION USING LSTM NETWORKS IN PYTHON JUPYTER NOTEBOOK</article-title></title-group><contrib-group content-type="authors"><contrib id="42" contrib-type="author" corresp="yes"><name><given-names>VIVEK PARKASH</given-names></name><xref ref-type="aff" rid="aff-1">1</xref><aff id="aff-1"><label>0</label><institution>Dyal Singh College</institution><country>India</country></aff></contrib></contrib-group><contrib-group content-type="editors"><contrib contrib-type="editor"/></contrib-group><pub-date pub-type="epub" data-type="pub" iso-8601-date="2023-05-27"><day>27</day><month>05</month><year iso-8601-date="2">2023</year></pub-date><volume>1</volume><elocation-id>V1-I2-2023</elocation-id><history><date date-type="received" iso-8601-date="2023-03-16"><day>16</day><month>03</month><year iso-8601-date="2023">2023</year></date><date date-type="revised" iso-8601-date="2023-03-23"><day>23</day><month>03</month><year iso-8601-date="2023"/></date><date date-type="accepted" iso-8601-date="2023-03-23"><day>23</day><month>03</month><year iso-8601-date="2023"/></date></history><permissions><copyright-statement>&#xA9;2023 VIVEK PARKASH Year Corresponding Author</copyright-statement><copyright-year>2023</copyright-year><copyright-holder>VIVEK PARKASH</copyright-holder><license href="https://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (ISRDO) and either DOI or URL of the article must be cited.<ext-link ext-link-type="uri" href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link></license-p></license></permissions><self-uri href="https://isrdo.org/journal/SRJSET/currentissue/mathematical-model-for-stock-price-prediction-using-lstm-networks-in-python-jupyter-notebook"/><abstract><p>Long short-term memory abbreviated as LSTM&#xD;
is an artificial neural network used in the fields of artificial&#xD;
intelligence and deep learning. I am going to LSTM (long short term&#xD;
memory) networks and python coding in jupyter notebook for price movement predictions&#xD;
for TCS stock listed on NSE. In the end it will be concluded that the predicted&#xD;
movement of TCS stock price is similar to the actual one. Moreover next 20 days&#xD;
opening prices will be calculated based on previous few days price data.</p></abstract><kwd-group kwd-group-type="author"><kwd>yahoo finance</kwd><kwd> long short term memory networks</kwd><kwd> keras</kwd><kwd> pandas</kwd><kwd> dataframe</kwd><kwd> deep learning</kwd><kwd> neural network</kwd></kwd-group><funding-group><funding-statement>Got funding from nowhere for this paper.</funding-statement></funding-group></article-meta></front><back><sec sec-type="data-availability"><title>Data Availability</title><p>&nbsp;data will be shared as required</p></sec><sec sec-type="COI-statement"><title>Conflicts of Interest</title><p>No Conflict of Interest</p></sec><sec sec-type="author-contributions"><title>Authors&#x2019; Contributions</title><p>Sole author Dr. Vivek Parkash  has done work in this research paper himself and fully own the responsibility.</p></sec><sec sec-type="funding-statement"><title>Funding Statement</title><p>Got funding from nowhere for this paper.</p></sec><sec sec-type="software-information"><title>software-information</title><p>used LSTM Python with different libraries and functions</p></sec><ack><title>Acknowledgments</title><p>I thank my family for the cooperation.</p></ack><ref-list content-type="authoryear"><ref id="1"><label>1</label><element-citation publication-type="journal"><p>[1] https://www.analyticsvidhya.com/blog/2021/12/stock-price-prediction-using-lstm/&#xD;
[2] https://towardsdatascience.com/lstm-for-google-stock-price-prediction-e35f5cc84165&#xD;
[3] https://www.datacamp.com/tutorial/lstm-python-stock-market&#xD;
[4] https://www.kaggle.com/code/faressayah/stock-market-analysis-prediction-using-lstm&#xD;
[5]HongjuYan and Hongbing Ouyang. Financial time series prediction based on deep learning. Wireless Personal Communications, 102(2):683&#x2013;700, 2018.&#xD;
[6] https://en.wikipedia.org/wiki/Long_short-term_memory&#xD;
[7]https://www.analyticsvidhya.com/blog/2021/03/introduction-to-long-short-term-memory-lstm/</p></element-citation></ref></ref-list></back></article>
