A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting
Given the far-reaching impact of the gold price on global financial markets, accurately predicting the gold price has become essential, with machine learning methods emerging as a prominent tool to tackle this challenge. Nonetheless, traditional single prediction models usually suffer from limited p...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10546955/ |
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| author | Cihai Qiu Yitian Zhang Xunrui Qian Chuhang Wu Jiacheng Lou Yang Chen Yansong Xi Weijie Zhang Zhenxi Gong |
| author_facet | Cihai Qiu Yitian Zhang Xunrui Qian Chuhang Wu Jiacheng Lou Yang Chen Yansong Xi Weijie Zhang Zhenxi Gong |
| author_sort | Cihai Qiu |
| collection | DOAJ |
| description | Given the far-reaching impact of the gold price on global financial markets, accurately predicting the gold price has become essential, with machine learning methods emerging as a prominent tool to tackle this challenge. Nonetheless, traditional single prediction models usually suffer from limited predictive performance and fail to capture complex variability of market behavior. Aiming to solve these limitations, an innovative two-stage hybrid deep integration framework that combines feature extraction and residual correction techniques is proposed with a view to predicting the gold price more accurately. The prediction effectiveness is enhanced by employing a variational modal decomposition to cluster time series data into three classes. The first stage employs variational mode decomposition to categorize time series data, improving computational efficiency and initial prediction accuracy. The second stage refines these predictions through a novel residual correction process, leveraging back propagation, long and short-term memory, and convolutional neural networks. In addition, through the in-depth analysis and processing of residuals, it is demonstrated that starvation of our method further improves the credibility of the prediction results, and effectively predicts the price movements of the four major gold markets. This approach not only provides a remarkably valuable perspective for policy makers, investors, and trading firms in the gold market, but also deals with the shortcomings of a single model in the face of complex market dynamics, and lays the foundation for the development of even more powerful forecasting models in the future. |
| format | Article |
| id | doaj-art-3f0839ac3a0648209bd35c0950df4d1b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3f0839ac3a0648209bd35c0950df4d1b2025-08-20T03:31:10ZengIEEEIEEE Access2169-35362024-01-0112855658557910.1109/ACCESS.2024.340883710546955A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price ForecastingCihai Qiu0Yitian Zhang1Xunrui Qian2Chuhang Wu3Jiacheng Lou4Yang Chen5Yansong Xi6Weijie Zhang7Zhenxi Gong8https://orcid.org/0009-0004-5623-4540School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, U.K.School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, U.K.College of Overseas Education, Changzhou University, Changzhou, Jiangsu, ChinaDepartment of Economics, University of Connecticut, Storrs, CT, USASchool of Mathematical, Physical and Computational Sciences, University of Reading, Reading, U.K.School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, U.K.Qian Xuesen College, Nanjing University of Science and Technology, Nanjing, ChinaNanjing University of Information Science and Technology, Nanjing, ChinaNanjing University of Information Science and Technology, Nanjing, ChinaGiven the far-reaching impact of the gold price on global financial markets, accurately predicting the gold price has become essential, with machine learning methods emerging as a prominent tool to tackle this challenge. Nonetheless, traditional single prediction models usually suffer from limited predictive performance and fail to capture complex variability of market behavior. Aiming to solve these limitations, an innovative two-stage hybrid deep integration framework that combines feature extraction and residual correction techniques is proposed with a view to predicting the gold price more accurately. The prediction effectiveness is enhanced by employing a variational modal decomposition to cluster time series data into three classes. The first stage employs variational mode decomposition to categorize time series data, improving computational efficiency and initial prediction accuracy. The second stage refines these predictions through a novel residual correction process, leveraging back propagation, long and short-term memory, and convolutional neural networks. In addition, through the in-depth analysis and processing of residuals, it is demonstrated that starvation of our method further improves the credibility of the prediction results, and effectively predicts the price movements of the four major gold markets. This approach not only provides a remarkably valuable perspective for policy makers, investors, and trading firms in the gold market, but also deals with the shortcomings of a single model in the face of complex market dynamics, and lays the foundation for the development of even more powerful forecasting models in the future.https://ieeexplore.ieee.org/document/10546955/Feature fusionintegration modelprice forecastresidual correction |
| spellingShingle | Cihai Qiu Yitian Zhang Xunrui Qian Chuhang Wu Jiacheng Lou Yang Chen Yansong Xi Weijie Zhang Zhenxi Gong A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting IEEE Access Feature fusion integration model price forecast residual correction |
| title | A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting |
| title_full | A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting |
| title_fullStr | A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting |
| title_full_unstemmed | A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting |
| title_short | A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting |
| title_sort | two stage deep fusion integration framework based on feature fusion and residual correction for gold price forecasting |
| topic | Feature fusion integration model price forecast residual correction |
| url | https://ieeexplore.ieee.org/document/10546955/ |
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