A Novel Hybrid Model Based on CEEMDAN and Bayesian Optimized LSTM for Financial Trend Prediction
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 Empiric...
| 出版年: | INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS |
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| 主要な著者: | , , , |
| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
SCIENCE & INFORMATION SAI ORGANIZATION LTD
2025
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| 主題: | |
| オンライン･アクセス: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001441772100001 |