Stock Market Forecasting Using LSTM Neural Network

Authors

  • Aditi Singh  Amity University, Chhattisgarh, India
  • Lavnika Markande  Amity University, Chhattisgarh, India

DOI:

https://doi.org/10.32628/CSEIT23903138

Keywords:

Recurrent Neural Network(RNN), Long Short Term Memory(LSTM), Stock Prediction, Artificial Intelligence

Abstract

This study investigates the widespread use of machine learning techniques, including recurrent neural networks (RNNs) and long short-term memory (LSTM) models to the analysis of stock market data. By utilizing RNN and LSTM model capabilities to identify temporal relationships and patterns in stock market data, it seeks to overcome conventional techniques' constraints. The research provides empirical proof of the efficiency of RNNs and LSTM models in enhancing investment decision-making by analyzing the project outcomes using real-world stock market data. The inclusion of RNNs and LSTM models in this research paper strengthens the exploration of machine learning techniques in stock market analysis.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-06-30

Issue

Volume 9, Issue 3, May-June-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Aditi Singh, Lavnika Markande, " Stock Market Forecasting Using LSTM Neural Network" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 3, pp.544-554, May-June-2023. Available at doi : https://doi.org/10.32628/CSEIT23903138