IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v176y2023ics0960077923010238.html
   My bibliography  Save this article

Generalized distance component method based on spatial amplitude and trend difference weighting operator for complex time series

Author

Listed:
  • Wang, Zhuo
  • Shang, Pengjian

Abstract

The generalized distance component (GDISCO) approach, which uses the property that the energy distance is rotationally invariant in high-dimensional space to measure the complexity of univariate or multivariate time series, was recently proposed. However, because this method disregards the time series’ spatial trend distribution and amplitude structure, this work offers a new spatial amplitude and trend difference weighting operator, abbreviated as WSATD. We present the weighted generalized distance component (WSATD-GDISCO) technique based on WSATD. Numerical simulations show that WSATD-GDISCO is effective for short and long time series, and it is robust to noise. Compared to the GDISCO method, the WSATD-GDISCO shows better performance to measure the dynamic characteristics of a system. Finally, the method is applied to investigate the relationship between EEG and alcoholics’ response to stimuli, as well as the long-term and short-term differences in stock market volatility in China and the United States.

Suggested Citation

  • Wang, Zhuo & Shang, Pengjian, 2023. "Generalized distance component method based on spatial amplitude and trend difference weighting operator for complex time series," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    • Handle: RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923010238
    DOI: 10.1016/j.chaos.2023.114122
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923010238
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2023.114122?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. Shoji, Isao & Nozawa, Masahiro, 2022. "Geometric analysis of nonlinear dynamics in application to financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    2. Sinan Xiao & Zhenzhou Lu & Pan Wang, 2018. "Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2703-2721, December.
    3. da Silva, Sidney T. & de Godoy, Moacir F. & Gregório, Michele L. & Viana, Ricardo L. & Batista, Antonio M., 2023. "Analysis of heartbeat time series via machine learning for detection of illnesses," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    4. Pyatt, Graham, 1976. "On the Interpretation and Disaggregation of Gini Coefficients," Economic Journal, Royal Economic Society, vol. 86(342), pages 243-255, June.
    5. Amit Shelef & Edna Schechtman, 2019. "A Gini-based time series analysis and test for reversibility," Statistical Papers, Springer, vol. 60(3), pages 687-716, June.
    6. Gabor J. Szekely & Maria L. Rizzo, 2005. "Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 151-183, September.
    7. Lamberti, P.W & Martin, M.T & Plastino, A & Rosso, O.A, 2004. "Intensive entropic non-triviality measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 334(1), pages 119-131.
    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. Francesco Andreoli & Eugenio Peluso, 2016. "So close yet so unequal: Reconsidering spatial inequality in U.S. cities," Working Papers 21/2016, University of Verona, Department of Economics.
    2. Aquino, Andre L.L. & Ramos, Heitor S. & Frery, Alejandro C. & Viana, Leonardo P. & Cavalcante, Tamer S.G. & Rosso, Osvaldo A., 2017. "Characterization of electric load with Information Theory quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 277-284.
    3. Bariviera, Aurelio F. & Font-Ferrer, Alejandro & Sorrosal-Forradellas, M. Teresa & Rosso, Osvaldo A., 2019. "An information theory perspective on the informational efficiency of gold price," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    4. José Lorenzo, 2002. "E-Index for measuring concentration," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 8(4), pages 357-361, November.
    5. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    6. Qiuqiong Huang & David Dawe & Scott Rozelle & Jikun Huang & Jinxia Wang, 2005. "Irrigation, poverty and inequality in rural China," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 49(2), pages 159-175, June.
    7. Bas van Leeuwen & Peter Foldvari, 2012. "The development of inequality and poverty in Indonesia, 1932-1999," Working Papers 0026, Utrecht University, Centre for Global Economic History.
    8. Fernandes, Leonardo H.S. & de Araujo, Fernando H.A. & Tabak, Benjamin M., 2021. "Insights from the (in)efficiency of Chinese sectoral indices during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    9. Masato Okamoto, 2009. "Decomposition of gini and multivariate gini indices," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 7(2), pages 153-177, June.
    10. Liss, Erik & Korpi, Martin & Wennberg, Karl, 2023. "Absolute income mobility and the effect of parent generation inequality: An extended decomposition approach," European Economic Review, Elsevier, vol. 152(C).
    11. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.
    12. Zdeňka Náglová & Tereza Horáková, 2017. "Position of the Bakery Enterprises in the Czech Republic According to Detailed Specification of the Businesses," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(5), pages 1719-1727.
    13. Montani, Fernando & Deleglise, Emilia B. & Rosso, Osvaldo A., 2014. "Efficiency characterization of a large neuronal network: A causal information approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 58-70.
    14. Goedde-Menke, Michael & Ingermann, Peter-Hendrik, 2024. "Loan officer specialization and credit defaults," Journal of Banking & Finance, Elsevier, vol. 161(C).
    15. Sudheesh K. Kattumannil & N. Sreelakshmi & N. Balakrishnan, 2022. "Non-Parametric Inference for Gini Covariance and its Variants," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 790-807, August.
    16. Maria Ana Lugo & Esfandiar Maasoumi, 2008. "Multidimensional Poverty Measures from an Information Theory Perspective," Working Papers 85, ECINEQ, Society for the Study of Economic Inequality.
    17. Emanuela Raffinetti & Elena Siletti & Achille Vernizzi, 2015. "On the Gini coefficient normalization when attributes with negative values are considered," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 507-521, September.
    18. Quessy, Jean-François, 2021. "A Szekely–Rizzo inequality for testing general copula homogeneity hypotheses," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    19. Fionn Murtagh & Pierre Legendre, 2014. "Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 274-295, October.
    20. Branko Milanovic & Shlomo Yitzhak, 2006. "Decomposing World Income Distribution: Does The World Have A Middle Class?," IBT Journal of Business Studies (JBS), Ilma University, Faculty of Management Science, vol. 2(2), pages 88-110.

    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:176:y:2023:i:c:s0960077923010238. 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.