MATHEMATICAL MODEL FOR STOCK PRICE PREDICTION USING LSTM NETWORKS IN PYTHON JUPYTER NOTEBOOK

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MATHEMATICAL MODEL FOR STOCK PRICE PREDICTION USING LSTM NETWORKS IN PYTHON JUPYTER NOTEBOOK

Subject: Mathematics
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  • Volume : 1 Issue : 2 2023
  • Page Number : 1-10
  • Publication : ISRDO

Published Manuscript

Title

MATHEMATICAL MODEL FOR STOCK PRICE PREDICTION USING LSTM NETWORKS IN PYTHON JUPYTER NOTEBOOK

Author

1. VIVEK PARKASH, Assistant Professor, Dyal Singh College, India

Abstract

Long short-term memory abbreviated as LSTM is an artificial neural network used in the fields of artificial intelligence and deep learning. I am going to LSTM (long short term memory) networks and python coding in jupyter notebook for price movement predictions for TCS stock listed on NSE. In the end it will be concluded that the predicted movement of TCS stock price is similar to the actual one. Moreover next 20 days opening prices will be calculated based on previous few days price data.

Keywords

yahoo finance long short term memory networks keras pandas dataframe deep learning neural network

Conclusion

So, our model has successfully predicted stock TCS move for the next 20 days. This graph shows that how well the share has moved during the prediction period. This above described model is for TCS stock. This model can be applied to any other stock. All we have to do is to import the corresponding stock data from yahoo finance. We can change other parameters accordingly and tweak the parameters to get better results.

Author Contrubution

Sole author Dr. Vivek Parkash has done work in this research paper himself and fully own the responsibility.

Funding

Got funding from nowhere for this paper.

Conflict of Interest

No Conflict of Interest

Data Sharing Statement

 data will be shared as required

Software And Tools Use

used LSTM Python with different libraries and functions

Acknowledgements

I thank my family for the cooperation.

Corresponding Author

VP
VIVEK PARKASH

Dyal Singh College, Assistant Professor, India

Copyright

Copyright: ©2024 Corresponding Author. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PARKASH, VIVEK. “MATHEMATICAL MODEL FOR STOCK PRICE PREDICTION USING LSTM NETWORKS IN PYTHON JUPYTER NOTEBOOK.” Scientific Research Journal of Science, Engineering and Technology, vol. 1, no. 2, 2023, pp. 1-10, https://isrdo.org/journal/SRJSET/currentissue/mathematical-model-for-stock-price-prediction-using-lstm-networks-in-python-jupyter-notebook

PARKASH, V. (2023). MATHEMATICAL MODEL FOR STOCK PRICE PREDICTION USING LSTM NETWORKS IN PYTHON JUPYTER NOTEBOOK. Scientific Research Journal of Science, Engineering and Technology, 1(2), 1-10. https://isrdo.org/journal/SRJSET/currentissue/mathematical-model-for-stock-price-prediction-using-lstm-networks-in-python-jupyter-notebook

PARKASH VIVEK, MATHEMATICAL MODEL FOR STOCK PRICE PREDICTION USING LSTM NETWORKS IN PYTHON JUPYTER NOTEBOOK, Scientific Research Journal of Science, Engineering and Technology 1, no. 2(2023): 1-10, https://isrdo.org/journal/SRJSET/currentissue/mathematical-model-for-stock-price-prediction-using-lstm-networks-in-python-jupyter-notebook

882

Total words

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Unique Words

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Avg Sentence Length

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Subjectivity

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Text Statistics

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  • Smog Index : 12
  • Flesch Kincaid Grade : 10.3
  • Coleman Liau Index : 12.64
  • Automated Readability Index : 13.4
  • Dale Chall Readability Score : 9.01
  • Difficult Words : 156
  • Linsear Write Formula : 13
  • Gunning Fog : 10.88
  • Text Standard : 12th and 13th grade

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