International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm
Considering the complexity pattern of the gold price, this paper adopts the decomposition-reconstruction-forecast-mergence scheme to perform the international gold price forecast. The original gold price data are decomposed into 12 intrinsic mode functions and a residual by the complete ensemble emp...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/1511479 |
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| _version_ | 1832552730151878656 |
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| author | Wanbo Lu Tingting Qiu Wenhui Shi Xiaojun Sun |
| author_facet | Wanbo Lu Tingting Qiu Wenhui Shi Xiaojun Sun |
| author_sort | Wanbo Lu |
| collection | DOAJ |
| description | Considering the complexity pattern of the gold price, this paper adopts the decomposition-reconstruction-forecast-mergence scheme to perform the international gold price forecast. The original gold price data are decomposed into 12 intrinsic mode functions and a residual by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method, and then the 13 sequences are reconstructed into a high-frequency subsequence (IMFH), a low-frequency subsequence (IMFL), and the residual (Res). According to the different characteristics of the subsequences, the IMFL and Res are forecasted by the support vector regression (SVR) model. Besides, in order to further improve the prediction accuracy of IMFH, we have developed a novel hybrid method based on the support vector regression (SVR) model and the grey wolf optimizer (GWO) algorithm with SVR for predicting the IMFH of gold prices, i.e., the CEEMDAN-GWO-SVR model. This hybrid model combines the methodology of complex systems with machine learning techniques, making it more appropriate for analyzing relationships such as high-frequency dependences and solving complex nonlinear problems. Finally, the final result is obtained by combining the forecasting results of the three subsequences. The empirical results show that the proposed model demonstrates the highest prediction ability among all of the investigated models in a comparison of prediction errors with other individual models. |
| format | Article |
| id | doaj-art-9d8d1ee7015d4fcd991775ec4399a3dc |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9d8d1ee7015d4fcd991775ec4399a3dc2025-02-03T05:58:04ZengWileyComplexity1099-05262022-01-01202210.1155/2022/1511479International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf AlgorithmWanbo Lu0Tingting Qiu1Wenhui Shi2Xiaojun Sun3School of Management Science and EngineeringSchool of StatisticsSchool of StatisticsSchool of StatisticsConsidering the complexity pattern of the gold price, this paper adopts the decomposition-reconstruction-forecast-mergence scheme to perform the international gold price forecast. The original gold price data are decomposed into 12 intrinsic mode functions and a residual by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method, and then the 13 sequences are reconstructed into a high-frequency subsequence (IMFH), a low-frequency subsequence (IMFL), and the residual (Res). According to the different characteristics of the subsequences, the IMFL and Res are forecasted by the support vector regression (SVR) model. Besides, in order to further improve the prediction accuracy of IMFH, we have developed a novel hybrid method based on the support vector regression (SVR) model and the grey wolf optimizer (GWO) algorithm with SVR for predicting the IMFH of gold prices, i.e., the CEEMDAN-GWO-SVR model. This hybrid model combines the methodology of complex systems with machine learning techniques, making it more appropriate for analyzing relationships such as high-frequency dependences and solving complex nonlinear problems. Finally, the final result is obtained by combining the forecasting results of the three subsequences. The empirical results show that the proposed model demonstrates the highest prediction ability among all of the investigated models in a comparison of prediction errors with other individual models.http://dx.doi.org/10.1155/2022/1511479 |
| spellingShingle | Wanbo Lu Tingting Qiu Wenhui Shi Xiaojun Sun International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm Complexity |
| title | International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm |
| title_full | International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm |
| title_fullStr | International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm |
| title_full_unstemmed | International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm |
| title_short | International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm |
| title_sort | international gold price forecast based on ceemdan and support vector regression with grey wolf algorithm |
| url | http://dx.doi.org/10.1155/2022/1511479 |
| work_keys_str_mv | AT wanbolu internationalgoldpriceforecastbasedonceemdanandsupportvectorregressionwithgreywolfalgorithm AT tingtingqiu internationalgoldpriceforecastbasedonceemdanandsupportvectorregressionwithgreywolfalgorithm AT wenhuishi internationalgoldpriceforecastbasedonceemdanandsupportvectorregressionwithgreywolfalgorithm AT xiaojunsun internationalgoldpriceforecastbasedonceemdanandsupportvectorregressionwithgreywolfalgorithm |