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Machine learning gold price forecasting
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AI
DOI：10.1080/17509653.2025.2453902.png)
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摘要
En 中文
Price projections for main metal commodities have been highly valued by many market players for a substantial period of time. Our research examines the daily reported price of gold in order to address the issue. The sample that is being investigated covers a period of ten years, beginning on 22 April 2014 and ending on 17 April 2024, and the price series that is being examined has significant business repercussions. When it comes to this particular scenario, Gaussian process regression models are constructed by using cross-validation approaches and Bayesian optimization methodologies. The strategies that are produced as a consequence are then used in order to supply price predictions. With a relative root mean square error of 0.8706%, our empirical prediction technique generates price projections that are relatively accurate for the out-of-sample measurement period that spans from 4 May 2022 to 17 April 2024. Investors and governments are provided with the knowledge they need to make informed decisions on the gold market due to the availability of models that anticipate prices.
Keyword:
Gold price
time-series forecasting
Gaussian process regression
Bayesian optimization
cross validation
Q31
C22
C53
期刊
IF:
2.6
论文数:
243
被引数:
739
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