A Novel Hybrid Model Based on CEEMDAN and Bayesian Optimized LSTM for Financial Trend Prediction

• 出版年: INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
• 主要な著者: Sun, Yu; Mutalib, Sofianita; Tian, Liwei
• フォーマット: 論文
• 言語: English
• 出版事項: SCIENCE & INFORMATION SAI ORGANIZATION LTD 2025
• 主題: Computer Science
• オンライン･アクセス: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001441772100001

Financial time series prediction is inherently complex due to its nonlinear, nonstationary, and highly volatile nature. This study introduces a novel CEEMDAN-BO-LSTM model within a decomposition-optimization-prediction- integration framework to address these challenges. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm decomposes the original series into high- frequency, medium-frequency, low-frequency, and trend components, enabling precise time window selection. Bayesian Optimization (BO) algorithm optimizes the parameters of a dual- layer Long Short-Term Memory (LSTM) network, enhancing prediction accuracy. By integrating predictions from each component, the model generates a comprehensive and reliable forecast. Experiments on 10 representative global stock indices reveal that the proposed model outperforms benchmark approaches across RMSE, MAE, MAPE, and R2 metrics. The CEEMDAN-BO-LSTM model demonstrates robustness and stability, effectively capturing market fluctuations and long-term trends, even under high volatility.