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Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series. / Wang, Han; Dong, Xinqi; Qu, Haichen et al.
In: Advances in Economics, Management and Political Sciences, Vol. 85, No. 1, 28.05.2024, p. 118-124.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Wang, H, Dong, X, Qu, H, Liao, J & Ma, D 2024, 'Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series', Advances in Economics, Management and Political Sciences, vol. 85, no. 1, pp. 118-124. https://doi.org/10.54254/2754-1169/85/20240857

APA

Wang, H., Dong, X., Qu, H., Liao, J., & Ma, D. (2024). Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series. Advances in Economics, Management and Political Sciences, 85(1), 118-124. https://doi.org/10.54254/2754-1169/85/20240857

Vancouver

Wang H, Dong X, Qu H, Liao J, Ma D. Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series. Advances in Economics, Management and Political Sciences. 2024 May 28;85(1):118-124. doi: 10.54254/2754-1169/85/20240857

Author

Wang, Han ; Dong, Xinqi ; Qu, Haichen et al. / Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series. In: Advances in Economics, Management and Political Sciences. 2024 ; Vol. 85, No. 1. pp. 118-124.

Bibtex

@article{2e515629267240c9ae6bd01312d52906,
title = "Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series",
abstract = "The price of gold, as an important precious metal, is highly volatile and uncertain as it is affected by the economic and political situation in the global market. Therefore, forecasting gold price is of great significance for investors, policy makers and economists. In this paper, an algorithm based on nonlinear weight decreasing PSO-SVR univariate time series prediction is proposed for forecasting gold price. The algorithm can help investors, policy makers and firms to understand market trends and price fluctuations and make more informed decisions. The algorithm is based on a nonlinear weighted particle swarm (IPSO) optimised support vector machine (SVM) time series model, which is trained with training set data and validated using test set data. Y-X scatter plots are plotted for the predicted and real values of the training set, and line plots of the predicted and real values of the training set are plotted in the coordinate system, and the results show that the algorithm is able to predict the price of the gold stock well, and the predicted and real values of the price of the gold stock can be very close to each other, both in the training set and the test set. The values of the model evaluation indexes R2, MAE, MBE and MAPE show that the algorithm can predict the gold stock price very well, and the prediction results in the test set do not deviate much from the training set. Therefore, the algorithm can provide useful market information and decision support for investors, policy makers and enterprises.",
author = "Han Wang and Xinqi Dong and Haichen Qu and Jiashu Liao and Danqing Ma",
year = "2024",
month = may,
day = "28",
doi = "10.54254/2754-1169/85/20240857",
language = "English",
volume = "85",
pages = "118--124",
journal = "Advances in Economics, Management and Political Sciences",
issn = "2754-1169",
publisher = "EWA Publishing",
number = "1",

}

RIS

TY - JOUR

T1 - Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series

AU - Wang, Han

AU - Dong, Xinqi

AU - Qu, Haichen

AU - Liao, Jiashu

AU - Ma, Danqing

PY - 2024/5/28

Y1 - 2024/5/28

N2 - The price of gold, as an important precious metal, is highly volatile and uncertain as it is affected by the economic and political situation in the global market. Therefore, forecasting gold price is of great significance for investors, policy makers and economists. In this paper, an algorithm based on nonlinear weight decreasing PSO-SVR univariate time series prediction is proposed for forecasting gold price. The algorithm can help investors, policy makers and firms to understand market trends and price fluctuations and make more informed decisions. The algorithm is based on a nonlinear weighted particle swarm (IPSO) optimised support vector machine (SVM) time series model, which is trained with training set data and validated using test set data. Y-X scatter plots are plotted for the predicted and real values of the training set, and line plots of the predicted and real values of the training set are plotted in the coordinate system, and the results show that the algorithm is able to predict the price of the gold stock well, and the predicted and real values of the price of the gold stock can be very close to each other, both in the training set and the test set. The values of the model evaluation indexes R2, MAE, MBE and MAPE show that the algorithm can predict the gold stock price very well, and the prediction results in the test set do not deviate much from the training set. Therefore, the algorithm can provide useful market information and decision support for investors, policy makers and enterprises.

AB - The price of gold, as an important precious metal, is highly volatile and uncertain as it is affected by the economic and political situation in the global market. Therefore, forecasting gold price is of great significance for investors, policy makers and economists. In this paper, an algorithm based on nonlinear weight decreasing PSO-SVR univariate time series prediction is proposed for forecasting gold price. The algorithm can help investors, policy makers and firms to understand market trends and price fluctuations and make more informed decisions. The algorithm is based on a nonlinear weighted particle swarm (IPSO) optimised support vector machine (SVM) time series model, which is trained with training set data and validated using test set data. Y-X scatter plots are plotted for the predicted and real values of the training set, and line plots of the predicted and real values of the training set are plotted in the coordinate system, and the results show that the algorithm is able to predict the price of the gold stock well, and the predicted and real values of the price of the gold stock can be very close to each other, both in the training set and the test set. The values of the model evaluation indexes R2, MAE, MBE and MAPE show that the algorithm can predict the gold stock price very well, and the prediction results in the test set do not deviate much from the training set. Therefore, the algorithm can provide useful market information and decision support for investors, policy makers and enterprises.

U2 - 10.54254/2754-1169/85/20240857

DO - 10.54254/2754-1169/85/20240857

M3 - Journal article

VL - 85

SP - 118

EP - 124

JO - Advances in Economics, Management and Political Sciences

JF - Advances in Economics, Management and Political Sciences

SN - 2754-1169

IS - 1

ER -