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DOI: 10.14569/IJACSA.2025.0160128
PDF

Improved Whale Optimization Algorithm with LSTM for Stock Index Prediction

Author 1: Yu Sun
Author 2: Sofianita Mutalib
Author 3: Liwei Tian

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 1, 2025.

  • Abstract and Keywords
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Abstract: After the COVID-19 pandemic, the global economy began to recover. However, stock market fluctuations continue to affect economic stability, making accurate predictions essential. This study proposes an Improved Whale Optimization Algorithm (IWOA) to optimize the parameters of the Long Short-Term Memory (LSTM) model, thereby enhancing stock index predictions. The IWOA improves upon the traditional Whale Optimization Algorithm (WOA) by integrating logistic chaotic mapping to increase population diversity and prevent premature convergence. Additionally, it incorporates a dynamic adjustment mechanism to balance global exploration and local exploitation, thus boosting optimization performance. Experiments conducted on five representative global stock indices demonstrate that the IWOA-LSTM model achieves higher accuracy and reliability compared to WOA-LSTM, LSTM, and RNN models. This highlights its value in predicting complex time-series data and supporting financial decision-making during economic recovery.

Keywords: Long short-term memory network; chaotic mapping; dynamic adjustment mechanism; improved whale optimization algorithm; financial time series forecasting

Yu Sun, Sofianita Mutalib and Liwei Tian, “Improved Whale Optimization Algorithm with LSTM for Stock Index Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160128

@article{Sun2025,
title = {Improved Whale Optimization Algorithm with LSTM for Stock Index Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160128},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160128},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Yu Sun and Sofianita Mutalib and Liwei Tian}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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