Abstract
Stock price prediction approach using Long Short-Term Memory (LSTM) networks. The proposed method utilizes historical stock price data as input and trains an LSTM model to predict future stock prices. The LSTM network architecture includes multiple layers of LSTM cells, which can capture long-term dependencies and patterns in the input data. The effectiveness of the proposed approach is evaluated using real-world stock price data from the S&P 500 index. Experimental results demonstrate that the LSTM-based approach can achieve promising prediction accuracy compared to other traditional methods. The proposed method can potentially benefit financial analysts, investors, and traders by providing useful insights for decision-making. Key Words: Financial analysis, Stock-Market, LSTM, Future prediction, Machine learning
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
