Decoding Stocks Patterns Using LSTM

Madhur Jain, Shilpi Jain and Ankit Gupta

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 3, 306-310

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.

Keywords: Stock market; ML; DL; LSTM; CNN; Trade (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410328
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i3:id:205

DOI: 10.32628/CSEIT2410328

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