1 Department of Finance and Operations Management, University of Tulsa, 800 S Tucker Dr, Tulsa, OK 74104, USA.
2 Department of Cyber Studies, University of Tulsa, 800 S Tucker Dr, Tulsa, OK 74104, USA.
World Journal of Advanced Research and Reviews, 2025, 27(03), 639–650
Article DOI: 10.30574/wjarr.2025.27.3.3182
Received on 02 August 2025; revised on 07 September 2025; accepted on 10 September 2025
In this paper, we provide a unique ensemble-based framework that combines sophisticated deep learning architectures and novel ensemble algorithms to improve forecast accuracy and robustness. The framework includes the Stacking Ensemble Meta-Model, which aggregates predictions as meta-features that are processed by a linear regression meta-model. We also introduce an Optimized Weighted Ensemble model, which uses constrained optimization to compute optimal weights for combining predictions from LSTM, CNN, and Hybrid CNN/LSTM-Attention-based models. Our results show that our proposed ensemble meta-model framework outperforms the optimized weighted ensemble model, with improved predictive accuracy across the evaluation metrices.
Stock Market Prediction; Deep Learning; LSTM; CNN; Attention Mechanism; Ensemble Learning; Stacking; Weighted Ensemble; Time Series Forecasting; Hybrid Models
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Md Talha Mohsin and Arafat Asim. Deep learning for stock price prediction: A comparative study of stacked and weighted ensemble models. World Journal of Advanced Research and Reviews, 2025, 27(03), 639–650. Article DOI: https://doi.org/10.30574/wjarr.2025.27.3.3182.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0