Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series DOI Creative Commons
Han Wang,

Xinqi Dong,

Haichen Qu

et al.

Advances in Economics Management and Political Sciences, Journal Year: 2024, Volume and Issue: 85(1), P. 118 - 124

Published: May 27, 2024

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

Language: Английский

Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series DOI Creative Commons
Han Wang,

Xinqi Dong,

Haichen Qu

et al.

Advances in Economics Management and Political Sciences, Journal Year: 2024, Volume and Issue: 85(1), P. 118 - 124

Published: May 27, 2024

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

Language: Английский

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