IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v174y2023ics0960077923007658.html
   My bibliography  Save this article

Measurement of information transfer based on phase increment transfer entropy

Author

Listed:
  • Lin, Guancen
  • Lin, Aijing
  • Mi, Yujia
  • Gu, Danlei

Abstract

The construction of time series networks in complex systems facilitates the investigation of information interaction mechanisms among subsystems. As widely used causal relationship measurement techniques, Granger causality (GC) is applicable to linear coupling, and information loss is a problem with transfer entropy based on permutation. In order to reasonably analyze the evolution of information transfer between nonlinear signals, we develop a novel causality inference method. In this paper, phase increment transfer entropy (PITE) is proposed, which performs increment symbolic processing on the phase series of signals, taking into account both sign and magnitude. PITE displays effectiveness in simulation experiments, and is more robust than baseline models that measure phase series information transfer of signals. Furthermore, PITE provided evidence that the information transfer between electroencephalogram (EEG) signals of healthy individuals and patients with Attention Deficit Hyperactivity Disorder (ADHD) differs. K-Nearest Neighbors (KNN) is utilized for categorizing subjects based on the causality network, demonstrating the effectiveness of PITE for assessing ADHD and quantifying brain information transfer. The proposed method will provide a novel idea for EEG-based disease research, and help to develop a broader understanding of causality networks.

Suggested Citation

  • Lin, Guancen & Lin, Aijing & Mi, Yujia & Gu, Danlei, 2023. "Measurement of information transfer based on phase increment transfer entropy," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:chsofr:v:174:y:2023:i:c:s0960077923007658
    DOI: 10.1016/j.chaos.2023.113864
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923007658
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2023.113864?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. Kang, Huan & Zhang, Xiaofeng & Zhang, Guangbin, 2021. "Phase permutation entropy: A complexity measure for nonlinear time series incorporating phase information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).
    2. Liu, Chang & Sun, Xiaolei & Wang, Jun & Li, Jianping & Chen, Jianming, 2021. "Multiscale information transmission between commodity markets: An EMD-Based transfer entropy network," Research in International Business and Finance, Elsevier, vol. 55(C).
    3. Troster, Victor & Shahbaz, Muhammad & Uddin, Gazi Salah, 2018. "Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis," Energy Economics, Elsevier, vol. 70(C), pages 440-452.
    4. Wang, Xin & Sun, Mei, 2021. "A novel prediction model of multi-layer symbolic pattern network: Based on causation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
    5. Podobnik, Boris & Horvatic, Davor & Lam Ng, Alfonso & Eugene Stanley, H. & Ivanov, Plamen Ch., 2008. "Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3954-3959.
    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. Lin, Guancen & Lin, Aijing, 2022. "Modified multiscale sample entropy and cross-sample entropy based on horizontal visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    2. İşcanoğlu-Çekiç, Ayşegül & Gülteki̇n, Havva, 2019. "Are cross-correlations between Turkish Stock Exchange and three major country indices multifractal or monofractal?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 978-990.
    3. Wang, Fanyi & Ma, Wanying & Mirza, Nawazish & Altuntaş, Mehmet, 2023. "Green financing, financial uncertainty, geopolitical risk, and oil prices volatility," Resources Policy, Elsevier, vol. 83(C).
    4. Chang, C-L. & McAleer, M.J. & Wang, Y-A., 2018. "Latent Volatility Granger Causality and Spillovers in Renewable Energy and Crude Oil ETFs," Econometric Institute Research Papers TI 2018-052/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Yuan, Di & Li, Sufang & Li, Rong & Zhang, Feipeng, 2022. "Economic policy uncertainty, oil and stock markets in BRIC: Evidence from quantiles analysis," Energy Economics, Elsevier, vol. 110(C).
    6. Zhang, Hao & Cai, Guixin & Yang, Dongxiao, 2020. "The impact of oil price shocks on clean energy stocks: Fresh evidence from multi-scale perspective," Energy, Elsevier, vol. 196(C).
    7. Wu, Yue & Shang, Pengjian & Chen, Shijian, 2019. "Modified multifractal large deviation spectrum based on CID for financial market system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1331-1342.
    8. Zhou, Wuhao & Xu, Yuanlu & Zhang, Li & Lin, Huifang, 2023. "Does public behavior and research development matters for economic growth in SMEs: Evidence from Chinese listed firms," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 107-119.
    9. El Alaoui, Marwane & Benbachir, Saâd, 2013. "Multifractal detrended cross-correlation analysis in the MENA area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5985-5993.
    10. Cai, Yifei & Chang, Hao-Wen & Xiang, Feiyun & Chang, Tsangyao, 2023. "Can precious metals hedge the risks of Sino–US political relation?–Evidence from Toda–Yamamoto causality test in quantiles," Finance Research Letters, Elsevier, vol. 58(PA).
    11. Xu, Meng & Shang, Pengjian, 2018. "Analysis of financial time series using multiscale entropy based on skewness and kurtosis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1543-1550.
    12. Muhammad Haseeb & Irwan Shah Zainal Abidin & Qazi Muhammad Adnan Hye & Nira Hariyatie Hartani, 2019. "The Impact of Renewable Energy on Economic Well-Being of Malaysia: Fresh Evidence from Auto Regressive Distributed Lag Bound Testing Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 9(1), pages 269-275.
    13. Durusu-Ciftci, Dilek & Soytas, Ugur & Nazlioglu, Saban, 2020. "Financial development and energy consumption in emerging markets: Smooth structural shifts and causal linkages," Energy Economics, Elsevier, vol. 87(C).
    14. Sueyoshi, Toshiyuki & Mo, Fei & Wang, Derek D., 2022. "Sustainable development of countries all over the world and the impact of renewable energy," Renewable Energy, Elsevier, vol. 184(C), pages 320-331.
    15. Ahmed Ali & Granberg Mark & Troster Victor & Uddin Gazi Salah, 2022. "Asymmetric dynamics between uncertainty and unemployment flows in the United States," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 26(1), pages 155-172, February.
    16. Mighri, Zouheir & Ragoubi, Hanen & Sarwar, Suleman & Wang, Yihan, 2022. "Quantile Granger causality between US stock market indices and precious metal prices," Resources Policy, Elsevier, vol. 76(C).
    17. Chen, Yufeng & Zheng, Biao & Qu, Fang, 2020. "Modeling the nexus of crude oil, new energy and rare earth in China: An asymmetric VAR-BEKK (DCC)-GARCH approach," Resources Policy, Elsevier, vol. 65(C).
    18. Ladislav Kristoufek, 2018. "Power-law cross-correlations: Issues, solutions and future challenges," Papers 1806.01616, arXiv.org.
    19. Dogan, Eyup & Altinoz, Buket & Madaleno, Mara & Taskin, Dilvin, 2020. "The impact of renewable energy consumption to economic growth: A replication and extension of Inglesi-Lotz (2016)," Energy Economics, Elsevier, vol. 90(C).
    20. Khan, Zeeshan & Malik, Muhammad Yousaf & Latif, Kashmala & Jiao, Zhilun, 2020. "Heterogeneous effect of eco-innovation and human capital on renewable & non-renewable energy consumption: Disaggregate analysis for G-7 countries," Energy, Elsevier, vol. 209(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:174:y:2023:i:c:s0960077923007658. 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.