Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression

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@Article{drachal:2023:Energies,

• author = "Krzysztof Drachal",
• title = "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression",
• journal = "Energies",
• year = "2023",
• volume = "16",
• number = "1",
• pages = "Article No. 4",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1996-1073",
• URL = "https://www.mdpi.com/1996-1073/16/1/4",
• DOI = "doi:10.3390/en16010004",
• abstract = "In this study, the crude oil spot price is forecast using Bayesian symbolic regression (BSR). In particular, the initial parameters specification of BSR is analysed. Contrary to the conventional approach to symbolic regression, which is based on genetic programming methods, BSR applies Bayesian algorithms to evolve the set of expressions (functions). This econometric method is able to deal with variable uncertainty (feature selection) issues in oil price forecasting. Secondly, this research seems to be the first application of BSR to oil price forecasting. Monthly data between January 1986 and April 2021 are analysed. As well as BSR, several other methods (also able to deal with variable uncertainty) are used as benchmark models, such as LASSO and ridge regressions, dynamic model averaging, and Bayesian model averaging. The more common ARIMA and naïve methods are also used, together with several time-varying parameter regressions. As a result, this research not only presents a novel and original application of the BSR method but also provides a concise and uniform comparison of the application of several popular forecasting methods for the crude oil spot price. Robustness checks are also performed to strengthen the obtained conclusions. It is found that the suitable selection of functions and operators for BSR initialization is an important, but not trivial, task. Unfortunately, BSR does not result in forecasts that are statistically significantly more accurate than the benchmark models. However, BSR is computationally faster than the genetic programming-based symbolic regression.",
• notes = "also known as \cite{en16010004}",

}

Genetic Programming entries for Krzysztof Drachal

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