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

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

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.