Forecasting Commodities Prices with the Bayesian Symbolic Regression Compared to Other Methods

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@InProceedings{Drachal:2023:BigData,

• author = "Krzysztof Drachal",
• booktitle = "2023 IEEE International Conference on Big Data (BigData)",
• title = "Forecasting Commodities Prices with the Bayesian Symbolic Regression Compared to Other Methods",
• year = "2023",
• pages = "3413--3421",
• abstract = "This study employs Bayesian Symbolic Regression (BSR) to forecasting spot prices of various commodities. This novel method exhibits promising potential as a forecasting tool, especially in the context of variable (feature) selection. Yet, there is no much research on symbolic regression as a forecasting tool for prices time-series in economics and finance. BSR offers valuable capabilities for tackling the challenges of variable selection (feature selection) in econometric modelling, as well as, it is expected to deal with some other issues smoothly. Herein, the analysis is specifically tailored to time-series data representing commodity markets. The accuracies of BSR models are compared with those of some alternative models: Symbolic Regression with Genetic Programming, Dynamic Model Averaging, LASSO regression, Time-Varying Parameters regression, ARIMA, no-change forecasting, etc. Unlike previous simulations of BSR, that relied on synthetic data, this study employs real-world data from commodities markets. The findings are expected to provide valuable insights for researchers and practitioners interested in applying BSR in econometric and financial contexts in the future.",
• keywords = "genetic algorithms, genetic programming, Uncertainty, Biological system modelling, Finance, Predictive models, Feature extraction, Data models, Bayesian econometrics, commodities prices, model averaging, symbolic regression, time-series forecasting, variable selection",
• DOI = "doi:10.1109/BigData59044.2023.10386819",
• month = dec,
• notes = "Also known as \cite{10386819}",

}

Genetic Programming entries for Krzysztof Drachal

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