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Communication Papers of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 37

Ensemble-based versus expert-assisted approach to carbon price features selection

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DOI: http://dx.doi.org/10.15439/2023F8389

Citation: Communication Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 37, pages 251–256 ()

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Abstract. The paper comments on two main issues. First, on a model for estimating the carbon price using multi-year market data. And second, on the consideration of two approaches to feature set exploitation. On the one hand, two ensemble machine-learning models with randomly selected feature sets are employed. On the other hand, a hybrid feature selection strategy follows domain expertise on which features should be explored. This minimizes the number of feature set combinations to be tested. The additional information for the predictions was the data from other commodity contracts, which could be easily introduced into the collection, as too many of them do not necessarily improve the estimates. The results of the experiments are promising: for the model based on SVR, the MAPE obtained was 2.09\% and 5.6\% for the following day and week price forecasts, respectively.

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