Estimation of International Gold Price by Fusing Deep/Shallow Machine Learning
In this work, we propose a new method that combines the support vector machine (SVM) and the long short-term memory (LSTM) model utilizing the theory of quotient space to predict the price of gold by leveraging the price factors that have supposedly an impact on the gold price. The Pearson correlati...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/6211861 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832563860278607872 |
|---|---|
| author | Wenjing Chen |
| author_facet | Wenjing Chen |
| author_sort | Wenjing Chen |
| collection | DOAJ |
| description | In this work, we propose a new method that combines the support vector machine (SVM) and the long short-term memory (LSTM) model utilizing the theory of quotient space to predict the price of gold by leveraging the price factors that have supposedly an impact on the gold price. The Pearson correlation coefficient is employed to measure the relations between nine price factors and gold price. The five price factors with larger correlation coefficients are picked. Then, by utilizing the Granger causality test, the gold price may change concerning the two price factors when time is a concern, which results in combining the results of the correlation analysis with the results of Granger causality leading to a total of seven price factors. Also, the gold price can be divided into the quarters of the year according to the theory of the quotient space and temporal attribute. With three granularities per month, a 3-layer quotient space is constructed based on the synthesized and calculated granularities. The proposed method provides the prediction results that are compared with the predicted values of some grey models (GM) and the actual gold price, respectively. The results suggested that the prediction results of gold price have a comparable lower error measurement and perform better. |
| format | Article |
| id | doaj-art-aa8a96d6f9b94b4a96d4e9d1ee1f1b97 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-aa8a96d6f9b94b4a96d4e9d1ee1f1b972025-02-03T01:12:22ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/6211861Estimation of International Gold Price by Fusing Deep/Shallow Machine LearningWenjing Chen0College of Finance and TradeIn this work, we propose a new method that combines the support vector machine (SVM) and the long short-term memory (LSTM) model utilizing the theory of quotient space to predict the price of gold by leveraging the price factors that have supposedly an impact on the gold price. The Pearson correlation coefficient is employed to measure the relations between nine price factors and gold price. The five price factors with larger correlation coefficients are picked. Then, by utilizing the Granger causality test, the gold price may change concerning the two price factors when time is a concern, which results in combining the results of the correlation analysis with the results of Granger causality leading to a total of seven price factors. Also, the gold price can be divided into the quarters of the year according to the theory of the quotient space and temporal attribute. With three granularities per month, a 3-layer quotient space is constructed based on the synthesized and calculated granularities. The proposed method provides the prediction results that are compared with the predicted values of some grey models (GM) and the actual gold price, respectively. The results suggested that the prediction results of gold price have a comparable lower error measurement and perform better.http://dx.doi.org/10.1155/2022/6211861 |
| spellingShingle | Wenjing Chen Estimation of International Gold Price by Fusing Deep/Shallow Machine Learning Journal of Advanced Transportation |
| title | Estimation of International Gold Price by Fusing Deep/Shallow Machine Learning |
| title_full | Estimation of International Gold Price by Fusing Deep/Shallow Machine Learning |
| title_fullStr | Estimation of International Gold Price by Fusing Deep/Shallow Machine Learning |
| title_full_unstemmed | Estimation of International Gold Price by Fusing Deep/Shallow Machine Learning |
| title_short | Estimation of International Gold Price by Fusing Deep/Shallow Machine Learning |
| title_sort | estimation of international gold price by fusing deep shallow machine learning |
| url | http://dx.doi.org/10.1155/2022/6211861 |
| work_keys_str_mv | AT wenjingchen estimationofinternationalgoldpricebyfusingdeepshallowmachinelearning |