Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression
It is non-trivial to predict the prices of precious metals since a number of factors can affect the fluctuations of precious metal prices. Either parametric models or machine learning models cannot accurately forecast the precious metal prices. Though deep learning approaches show their strengths in...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8943215/ |
| _version_ | 1818857106302828544 |
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| author | Junhao Zhou Zhanhong He Ya Nan Song Hao Wang Xiaoping Yang Wenjuan Lian Hong-Ning Dai |
| author_facet | Junhao Zhou Zhanhong He Ya Nan Song Hao Wang Xiaoping Yang Wenjuan Lian Hong-Ning Dai |
| author_sort | Junhao Zhou |
| collection | DOAJ |
| description | It is non-trivial to predict the prices of precious metals since a number of factors can affect the fluctuations of precious metal prices. Either parametric models or machine learning models cannot accurately forecast the precious metal prices. Though deep learning approaches show their strengths in extracting key features from complicated data, they have the limitations of learning localization and losing some temporal and spatial features. The recent advances in attention mechanisms bring the opportunities to overcome the limitation of deep learning models. In this paper, we originally propose a Regularization Self-Attention Regression Model for precious metal price prediction. In particular, the proposed RSAR model consists of convolutional neural network (CNN) component and Long Short-Term Memory Neural Networks (LSTM) component. Integrating with self-attention mechanism, this model can extract both spatial and temporal features from precious metal price data. Meanwhile, the proper configuration of regularization functions can also lead to the further performance improvement. Extensive experiments on realistic precious metal price dataset show that our proposed approach outperforms other conventional machine learning and deep learning methods. |
| first_indexed | 2024-12-19T08:35:07Z |
| format | Article |
| id | doaj.art-2b3f4567faa540c6b992a03ffc20e0d0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:35:07Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2b3f4567faa540c6b992a03ffc20e0d02022-12-21T20:29:03ZengIEEEIEEE Access2169-35362020-01-0182178218710.1109/ACCESS.2019.29622028943215Precious Metal Price Prediction Based on Deep Regularization Self-Attention RegressionJunhao Zhou0https://orcid.org/0000-0001-9309-8315Zhanhong He1https://orcid.org/0000-0001-8806-7135Ya Nan Song2https://orcid.org/0000-0002-5092-0329Hao Wang3https://orcid.org/0000-0001-9301-5989Xiaoping Yang4https://orcid.org/0000-0002-7030-7046Wenjuan Lian5https://orcid.org/0000-0002-5339-1303Hong-Ning Dai6https://orcid.org/0000-0001-6165-4196Faculty of Information Technology, Macau University of Science and Technology, Macao, ChinaDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong KongSchool of Business, Macau University of Science and Technology, Macao, ChinaDepartment of Computer Science, Norwegian University of Science and Technology, Gjøvik, NorwayInstitute of Modern Economics and Management, Zhejiang Yuexiu University of Foreign Languages, Shaoxing, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaFaculty of Information Technology, Macau University of Science and Technology, Macao, ChinaIt is non-trivial to predict the prices of precious metals since a number of factors can affect the fluctuations of precious metal prices. Either parametric models or machine learning models cannot accurately forecast the precious metal prices. Though deep learning approaches show their strengths in extracting key features from complicated data, they have the limitations of learning localization and losing some temporal and spatial features. The recent advances in attention mechanisms bring the opportunities to overcome the limitation of deep learning models. In this paper, we originally propose a Regularization Self-Attention Regression Model for precious metal price prediction. In particular, the proposed RSAR model consists of convolutional neural network (CNN) component and Long Short-Term Memory Neural Networks (LSTM) component. Integrating with self-attention mechanism, this model can extract both spatial and temporal features from precious metal price data. Meanwhile, the proper configuration of regularization functions can also lead to the further performance improvement. Extensive experiments on realistic precious metal price dataset show that our proposed approach outperforms other conventional machine learning and deep learning methods.https://ieeexplore.ieee.org/document/8943215/Long short-term memoryconvolutional neural networkattention mechanismfinancial data analysisdeep learning |
| spellingShingle | Junhao Zhou Zhanhong He Ya Nan Song Hao Wang Xiaoping Yang Wenjuan Lian Hong-Ning Dai Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression IEEE Access Long short-term memory convolutional neural network attention mechanism financial data analysis deep learning |
| title | Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression |
| title_full | Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression |
| title_fullStr | Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression |
| title_full_unstemmed | Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression |
| title_short | Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression |
| title_sort | precious metal price prediction based on deep regularization self attention regression |
| topic | Long short-term memory convolutional neural network attention mechanism financial data analysis deep learning |
| url | https://ieeexplore.ieee.org/document/8943215/ |
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