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• W4362610227 abstract "Steam coal is the blood of China industry. Forecasting steam coal prices accurately and reliably is of great significance to the stable development of China’s economy. For the predictive model of existing steam coal prices, it is difficult to dig the law of nonlinearity of power coal price data and with poor stability. To address the problems that steam coal price features are highly nonlinear and models lack robustness, Laplacian kernel–log hyperbolic loss–Ridge regression (LK-LC-Ridge-Ensemble) model is proposed, which uses ensemble learning model for steam coal price prediction. First, in each sliding window, two kinds of correlation coefficient are employed to identify the optimal time interval, while the optimal feature set is selected to reduce the data dimension. Second, the Laplace kernel functions are adopted for constructing kernel Ridge regression (LK-Ridge), which boosts the capacity to learn nonlinear laws; the logarithmic loss function is introduced to form the LK-LC-Ridge to enhance the robustness. Finally, the prediction results of each single regression models are utilized to build a results matrix that is input into the meta-model SVR for ensemble learning, which further develops the model performance. Empirical results from three typical steam coal price datasets indicate that the proposed ensemble strategy is reliable for the model performance enhancement. Furthermore, the proposed model outperforms all single primitive models including accuracy of prediction results and robustness of model. Grouping cross-comparison between the different models suggests that the proposed ensemble model is more accurate and robust for steam coal price forecasting." @default.
• W4362610227 created "2023-04-06" @default.
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• W4362610227 date "2023-01-01" @default.
• W4362610227 modified "2023-10-17" @default.
• W4362610227 title "STEAM COAL PRICE FORECASTING VIA LK-LC RIDGE REGRESSION ENSEMBLE LEARNING" @default.
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• W4362610227 doi "https://doi.org/10.1142/s0218348x23401412" @default.
• W4362610227 hasPublicationYear "2023" @default.
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