Novel Compositional Data's Grey Model for Structurally Forecasting Arctic Crude Oil Import
Pan Qilong, Yin Jieru, and Xiao Xinping

TL;DR
This paper introduces a new compositional data grey model combined with traditional grey modeling and differential evolution to forecast Arctic crude oil imports, demonstrating improved short-term accuracy and revealing increasing Arctic imports for China.
Contribution
A novel compositional data grey model is developed, integrating Aitchison geometry and differential evolution for robust short-term forecasting of Arctic crude oil imports.
Findings
The model outperforms existing models in short-term forecasting accuracy.
China's crude oil imports from the Arctic are projected to increase significantly.
The model confirms the changing structure of China's crude oil import sources.
Abstract
The reserve of crude oil in the Arctic area is abundant. Ice melting is making it possible to have intermediate access to the Arctic crude oil and its transportation. A novel compositional data's grey model is proposed in this paper to structurally forecast Arctic crude oil import. Firstly, the general accumulative operation sequence of multivariate compositional data is defined according to Aitchison geometry, then obtaining the novel model with the form of the compositional data vectors. Secondly, this paper studies the least square parameter estimation of the model. The novel model is deduced and selected as the time-response expression of the solution. Thirdly, this paper infuses the novel model with traditional grey model to improve its robustness. Differential Evolution algorithm is introduced to determine the optimal value of the general matrix. Lastly, two validation examples…
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Taxonomy
TopicsGrey System Theory Applications
