LSTM model for stock price prediction
LSTM model effectively predicts stock prices, aiding investors with accurate forecasts using machine learning techniques.
What it examines
This book-chapter explores the use of machine learning, specifically the LSTM model, for stock price prediction. It aims to improve forecast accuracy and aid investors by combining mathematical functions and external factors in time-series prediction.
What it concludes
The results suggest that LSTM models are highly effective for stock price prediction, benefiting investors. Future research could explore integrating more external factors. Potential applications include financial market analysis and automated trading systems.
Evidence objects
The results suggest that LSTM models are highly effective for stock price prediction, benefiting investors. Future research could explore integrating more external factors. Potential applications include financial market analysis and automated trading systems.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
LSTM model for stock price prediction | 11 | Advances in AI for Biomed Skip to main content Breadcrumbs Section. Click here to navigate to respective pages. Chapter Chapter LSTM model for stock price prediction DOI link for LSTM model for stock price prediction LSTM model for stock price prediction By Nissankara Lakshmi Prasanna , Rajesh Babu Yallamanda , Rama devi Gunnam , T. Kameswara Rao Book Advances in AI for Biomedical Instrumentation, Electronics and Computing Click here to navigate to parent product. Edition 1st Edition First Published 2024 Imprint CRC Press Pages 6 eBook ISBN 9781032644752 Share ABSTRACT Stock price forecasting uses machine learning effectively. To make wiser and more accurate stock market decisions, it is mandatory to forecast stock prices. In order to im-prove stock forecast accuracy and generate lucrative trades, a stock price prediction system is suggested that combines mathematical functions, machine learning, and other external aspects. In many practical
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