IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v162y2022ics0960077922006324.html
   My bibliography  Save this article

Interrelation measurement based on the multi-layer limited penetrable horizontal visibility graph

Author

Listed:
  • Wang, Minggang
  • Hua, Chenyu
  • Zhu, Mengrui
  • Xie, Shangshan
  • Xu, Hua
  • Vilela, André L.M.
  • Tian, Lixin

Abstract

Interrelation measurement is the basis of big data mining. This paper proposes an efficient method to measure the dynamic correlation and synchronicity relationship of multidimensional data using the microscopic topological structure of a multi-layer network. In order to measure the dynamic correlation between multidimensional data, multidimensional data are transformed into a time-varying multi-layer limited penetrable horizontal visibility graph network. On this basis, a time-varying correlation measurement index of multidimensional data based on the microscopic structure of the interlayer network connection is proposed. In addition, based on the degree distribution of each layer and information entropy theory, a time-varying information measurement index of multidimensional data is introduced. Further, to measure the synchronicity relationship between multidimensional data, time-delay parameters are defined, and a method to transform multidimensional data into a delay time-varying multi-layer limited penetrable horizontal visibility graph network is developed. A symmetrical and antisymmetrical combinations index is defined to measure the synchronicity relationship and to determine which system leads the others. Numerical simulation verifies the effectiveness of the proposed index and the proposed method's robustness to handle data disturbed by noise. Finally, an empirical analysis is conducted using the price data of the energy and carbon markets. The dynamic relationship between the crude oil future and gasoline future market is obtained. The dynamic information spillover effect between the carbon and energy markets is analyzed.

Suggested Citation

  • Wang, Minggang & Hua, Chenyu & Zhu, Mengrui & Xie, Shangshan & Xu, Hua & Vilela, André L.M. & Tian, Lixin, 2022. "Interrelation measurement based on the multi-layer limited penetrable horizontal visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    • Handle: RePEc:eee:chsofr:v:162:y:2022:i:c:s0960077922006324
    DOI: 10.1016/j.chaos.2022.112422
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922006324
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.112422?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cui, Huizi & Zhou, Lingge & Li, Yan & Kang, Bingyi, 2022. "Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Reboredo, Juan C., 2014. "Volatility spillovers between the oil market and the European Union carbon emission market," Economic Modelling, Elsevier, vol. 36(C), pages 229-234.
    3. Hua Xu & Minggang Wang & Weiguo Yang, 2020. "Information Linkage between Carbon and Energy Markets: Multiplex Recurrence Network Approach," Complexity, Hindawi, vol. 2020, pages 1-12, August.
    4. Pham, Tuan D., 2020. "Fuzzy cross and fuzzy joint recurrence plots," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    5. Li, Xiuming & Sun, Mei & Gao, Cuixia & Han, Dun & Wang, Minggang, 2018. "The parametric modified limited penetrable visibility graph for constructing complex networks from time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1097-1106.
    6. Wang, Minggang & Tian, Lixin, 2016. "From time series to complex networks: The phase space coarse graining," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 456-468.
    7. Bezsudnov, I.V. & Snarskii, A.A., 2014. "From the time series to the complex networks: The parametric natural visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 53-60.
    8. Hu, Beibei & Ding, Yang & Dong, Xianlei & Bu, Yi & Ding, Ying, 2021. "On the relationship between download and citation counts: An introduction of Granger-causality inference," Journal of Informetrics, Elsevier, vol. 15(2).
    9. Gori, F. & Ludovisi, D. & Cerritelli, P.F., 2007. "Forecast of oil price and consumption in the short term under three scenarios: Parabolic, linear and chaotic behaviour," Energy, Elsevier, vol. 32(7), pages 1291-1296.
    10. Du, Ruijin & Wang, Ya & Dong, Gaogao & Tian, Lixin & Liu, Yixiao & Wang, Minggang & Fang, Guochang, 2017. "A complex network perspective on interrelations and evolution features of international oil trade, 2002–2013," Applied Energy, Elsevier, vol. 196(C), pages 142-151.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    2. Yaqi Wu & Chen Zhang & Po Yun & Dandan Zhu & Wei Cao & Zulfiqar Ali Wagan, 2021. "Time–frequency analysis of the interaction mechanism between European carbon and crude oil markets," Energy & Environment, , vol. 32(7), pages 1331-1357, November.
    3. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    4. An, Sufang & Gao, Xiangyun & Jiang, Meihui & Sun, Xiaoqi, 2018. "Multivariate financial time series in the light of complex network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1241-1255.
    5. Wang, Minggang & Xu, Hua & Tian, Lixin & Eugene Stanley, H., 2018. "Degree distributions and motif profiles of limited penetrable horizontal visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 620-634.
    6. An, Sufang & Gao, Xiangyun & An, Haizhong & An, Feng & Sun, Qingru & Liu, Siyao, 2020. "Windowed volatility spillover effects among crude oil prices," Energy, Elsevier, vol. 200(C).
    7. Shengli, Liu & Yongtu, Liang, 2019. "Exploring the temporal structure of time series data for hazardous liquid pipeline incidents based on complex network theory," International Journal of Critical Infrastructure Protection, Elsevier, vol. 26(C).
    8. Minggang Wang & Chenyu Hua & Hua Xu, 2022. "Dynamic Linkages among Carbon, Energy and Financial Markets: Multiplex Recurrence Network Approach," Mathematics, MDPI, vol. 10(11), pages 1-23, May.
    9. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    10. Zhen Zhang & Minggang Wang & Lixin Tian & Wenbin Zhang, 2017. "Research on the development efficiency of regional high-end talent in China: A complex network approach," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-18, December.
    11. Wei, Yu & Wang, Yizhi & Vigne, Samuel A. & Ma, Zhenyu, 2023. "Alarming contagion effects: The dangerous ripple effect of extreme price spillovers across crude oil, carbon emission allowance, and agriculture futures markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    12. Haugom, Erik & Mydland, Ørjan & Pichler, Alois, 2016. "Long term oil prices," Energy Economics, Elsevier, vol. 58(C), pages 84-94.
    13. Fang, Sheng & Lu, Xinsheng & Li, Jianfeng & Qu, Ling, 2018. "Multifractal detrended cross-correlation analysis of carbon emission allowance and stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 551-566.
    14. Green, Rikard & Larsson, Karl & Lunina, Veronika & Nilsson, Birger, 2018. "Cross-commodity news transmission and volatility spillovers in the German energy markets," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 231-243.
    15. Adekoya, Oluwasegun B. & Oliyide, Johnson A. & Noman, Ambreen, 2021. "The volatility connectedness of the EU carbon market with commodity and financial markets in time- and frequency-domain: The role of the U.S. economic policy uncertainty," Resources Policy, Elsevier, vol. 74(C).
    16. Yi Yao & Lixin Tian & Guangxi Cao, 2022. "The Information Spillover among the Carbon Market, Energy Market, and Stock Market: A Case Study of China’s Pilot Carbon Markets," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    17. Shi Chen & Wolfgang Karl Hardle & Brenda L'opez Cabrera, 2020. "Regularization Approach for Network Modeling of German Power Derivative Market," Papers 2009.09739, arXiv.org.
    18. An, Sufang & Gao, Xiangyun & An, Haizhong & Liu, Siyao & Sun, Qingru & Jia, Nanfei, 2020. "Dynamic volatility spillovers among bulk mineral commodities: A network method," Resources Policy, Elsevier, vol. 66(C).
    19. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    20. Chu, Baoju & Dong, Yizhe & Liu, Yaorong & Ma, Diandian & Wang, Tianju, 2024. "Does China's emission trading scheme affect corporate financial performance: Evidence from a quasi-natural experiment," Economic Modelling, Elsevier, vol. 132(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:162:y:2022:i:c:s0960077922006324. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.