Dynamic learning rate adjustment using volatility in LSTM models for KLCI forecasting

Dynamic learning rate adjustment using volatility in LSTM models for KLCI forecasting

The prediction of financial market behaviour constitutes a multifaceted challenge, attributable to the underlying volatility and non-linear characteristics inherent within market data. Long Short-Term Memory (LSTM) models have demonstrated efficacy in capturing these complexities. This study propo...

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Bibliographic Details
Main Authors: Abang Mohammad Hudzaifah, Abang Shakawi, Ani, Shabri
Format: Article
Language:en
Published: Lviv Polytechnic National University 2025
Subjects:
QA Mathematics
Online Access:http://ir.unimas.my/id/eprint/48254/1/Published%20Paper_MMC.pdf
http://ir.unimas.my/id/eprint/48254/
https://science.lpnu.ua/mmc/all-volumes-and-issues/volume-12-number-1-2025/dynamic-learning-rate-adjustment-using-volatility
https://doi.org/10.23939/mmc2025.01.158
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http://ir.unimas.my/id/eprint/48254/1/Published%20Paper_MMC.pdf
http://ir.unimas.my/id/eprint/48254/
https://science.lpnu.ua/mmc/all-volumes-and-issues/volume-12-number-1-2025/dynamic-learning-rate-adjustment-using-volatility
https://doi.org/10.23939/mmc2025.01.158

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