Enhancing Stock Index Forecasting with LSTM using Volatility-Weighted Input Features
Today’s local and global stock markets are confronted with numerous challenges, such as the difficulty in accurately predicting stock prices and extracting useful features. While many researchers have successfully employed long short-term memory (LSTM) models for stock price prediction, there is sti...
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| Main Authors: | , |
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| Format: | Article |
| Language: | en |
| Published: |
PENERBIT UTM PRESS
2025
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/48255/1/Published%20Paper_Matematika.pdf http://ir.unimas.my/id/eprint/48255/ https://matematika.utm.my/index.php/matematika/article/view/1615 https://doi.org/10.11113/matematika.v41.n1.1615 |
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