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Analysis about the seasonality of China's crude oil import based on X-12-ARIMA

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  • Zhou, Zhong-bing
  • Dong, Xiu-cheng

Abstract

The aim of this study is to examine the potential seasonality of China's crude oil import in hope of helping the stakeholders with inventory control and production planning. In order to proximately achieve the goal, X-12-ARIMA method was used to adjust the monthly series and the quarterly series of China's crude oil net import in the last 16 years. The results show that the quarterly series is better than the monthly series in terms of seasonality adjustment; the seasonal factors tend to be positive in spring and summer quarters while negative in fall and winter quarters; and the former three seasonal factors are growing stronger while the winter factor weaker in recent years.

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  • Zhou, Zhong-bing & Dong, Xiu-cheng, 2012. "Analysis about the seasonality of China's crude oil import based on X-12-ARIMA," Energy, Elsevier, vol. 42(1), pages 281-288.
    • Handle: RePEc:eee:energy:v:42:y:2012:i:1:p:281-288
    DOI: 10.1016/j.energy.2012.03.058
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    Cited by:

    1. Vaona, Andrea, 2016. "The effect of renewable energy generation on import demand," Renewable Energy, Elsevier, vol. 86(C), pages 354-359.
    2. Zhaoyang Kong & Xiucheng Dong & Zhongbing Zhou, 2015. "Seasonal Imbalances in Natural Gas Imports in Major Northeast Asian Countries: Variations, Reasons, Outlooks and Countermeasures," Sustainability, MDPI, vol. 7(2), pages 1-22, February.
    3. Wang, Minggang & Tian, Lixin & Du, Ruijin, 2016. "Research on the interaction patterns among the global crude oil import dependency countries: A complex network approach," Applied Energy, Elsevier, vol. 180(C), pages 779-791.
    4. Inchauspe, Julian & Li, Jun & Park, Jason, 2020. "Seasonal patterns of global oil consumption: Implications for long term energy policy," Journal of Policy Modeling, Elsevier, vol. 42(3), pages 536-556.
    5. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).
    6. Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.

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