Decoding Stocks Patterns Using LSTM

Dr. Madhur Jain; Shilpi Jain; Ankit Gupta

doi:10.32628/CSEIT2410328

Authors

Dr. Madhur Jain Assistant Professor, Department of IT, BPIT, University of Delhi, Delhi, India Author
Shilpi Jain Assistant Professor, Department of Mathematics, ARSD College, University of Delhi, Delhi, India Author
Ankit Gupta Student, Department of IT, BPIT, University of Delhi, Delhi, India Author

DOI:

https://doi.org/10.32628/CSEIT2410328

Keywords:

Stock market, ML, DL, LSTM, CNN, Trade

Abstract

Decoding stocks is extensively utilized in the financial sector by numerous organizations. It is volatile in nature, so it’s tough to predict the prices of stock. Numerous methodologies exist for tackling this task, including logistic regression, support vector machines (SVM), autoregressive conditional heteroskedasticity (ARCH) models, recurrent neural network (RNN), convolutional neural networks (CNN), backpropagation, Naïve Bayes, among others. Among these, Long Short-Term Memory (LSTM) stands out as particularly adept at handling time series data. The primary aim is to discern prevailing market trends and achieve accurate stock price forecasts. Leveraging LSTM and RNN , we strive for error free stock price predictions, with promising results.

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