Coal price forecasting using complete ensemble empirical mode decomposition and stacking-based ensemble learning with semisupervised data processing

Jing Tang

doi:10.1002/ese3.1660

2024-01-04

DOI：10.1002/ese3.1660

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摘要

En 中文

Globally, coal is a critical energy source, and the profits of related enterprises are highly related to changes in the coal price. A robust coal purchasing cost forecasting method may enhance the coal purchasing strategies of coal-consuming enterprises and obtain key information for reducing global carbon emissions. However, forecasting the price of coal is a challenging task due to the noise and high random fluctuation of coal price data. To overcome these obstacles, this research proposes a novel forecasting method combining data decomposition, semisupervised feature engineering, and ensemble learning to forecast coal prices. Initially, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is employed to decompose the coal price series to reduce the complexity. Second, considering the fluctuation of coal price is influenced by various factors (such as transportation cost and coal mine production), the proposed system incorporates an adaptive data fusion module to fuse data from multiple sources. Finally, a stacking-based ensemble learning model is adopted in the method to increase the forecasting accuracy by combining the forecasting results of multiple models. The Bohai-Rim Steam-Coal Price Index was used to validate the proposed method, and the result of the case study shows that the proposed method provides superior performance than the other nine baseline models in all measured indices. The outcomes of ablation tests indicate the precision of each algorithm is improved by combining CEEMDAN, which proves that the decomposition algorithm is necessary. Coal price forecasting method based on semisupervised techniques, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, and stacking ensemble learning.image

Keyword:

coal price forecasting

decomposition

ensemble learning

semisupervised learning