IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v35y2018i2d10.1007_s00357-018-9258-x.html
   My bibliography  Save this article

A Copula Based ICA Algorithm and Its Application to Time Series Clustering

Author

Listed:
  • Jafar Rahmanishamsi

    (Yazd University)

  • Ali Dolati

    (Yazd University)

  • Masoudreza R. Aghabozorgi

    (Yazd University)

Abstract

Independent component analysis (ICA) is a method to recover the original independent variables from the linear transformations of the observations. Most of ICA algorithms are formulated as an optimization of a contrast function which minimizes the cross-dependency among the components. In this paper, we propose an innovative algorithm for performing ICA problem which uses a contrast function based on the Hoeffding’s measure of pairwise dependence. This measure takes its minimum if and only if the random variables are independent, and takes its maximum if and only if one of the variable is a function of the other. Since the Hoeffding’s index is computed based on the rank values rather than the actual values of the data, it is significantly robust to the outliers and performs well even in the presence of noise. The proposed algorithm is evaluated using simulated data. The algorithm is utilized as a pre-processing method for clustering of trends in time series data. This pre-processing technique establish new components from original observations which have adequate information trend of time series. For illustrative purposes, the proposed methodology is applied to clustering of two real data sets involving financial time series.

Suggested Citation

  • Jafar Rahmanishamsi & Ali Dolati & Masoudreza R. Aghabozorgi, 2018. "A Copula Based ICA Algorithm and Its Application to Time Series Clustering," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 230-249, July.
  • Handle: RePEc:spr:jclass:v:35:y:2018:i:2:d:10.1007_s00357-018-9258-x
    DOI: 10.1007/s00357-018-9258-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00357-018-9258-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00357-018-9258-x?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. Gaißer, Sandra & Ruppert, Martin & Schmid, Friedrich, 2010. "A multivariate version of Hoeffding's Phi-Square," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2571-2586, November.
    2. Juan Vilar & José Vilar & Sonia Pértega, 2009. "Classifying Time Series Data: A Nonparametric Approach," Journal of Classification, Springer;The Classification Society, vol. 26(1), pages 3-28, April.
    3. Sonia Díaz & José Vilar, 2010. "Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 333-362, November.
    4. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    5. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2015. "Clustering of time series via non-parametric tail dependence estimation," Statistical Papers, Springer, vol. 56(3), pages 701-721, August.
    6. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari, 2021. "Trimmed fuzzy clustering of financial time series based on dynamic time warping," Annals of Operations Research, Springer, vol. 299(1), pages 1379-1395, April.
    2. Douglas L. Steinley, 2018. "Editorial," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 391-393, October.

    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. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    2. Zhang, Yi & Cheng, Chuntian & Cai, Huaxiang & Jin, Xiaoyu & Jia, Zebin & Wu, Xinyu & Su, Huaying & Yang, Tiantian, 2022. "Long-term stochastic model predictive control and efficiency assessment for hydro-wind-solar renewable energy supply system," Applied Energy, Elsevier, vol. 316(C).
    3. Ozan Cinar & Ozlem Ilk & Cem Iyigun, 2018. "Clustering of short time-course gene expression data with dissimilar replicates," Annals of Operations Research, Springer, vol. 263(1), pages 405-428, April.
    4. Lyu, Guowei & Bertolini, Luca & Pfeffer, Karin, 2016. "Developing a TOD typology for Beijing metro station areas," Journal of Transport Geography, Elsevier, vol. 55(C), pages 40-50.
    5. Giovanni De Luca & Paola Zuccolotto, 2017. "Dynamic tail dependence clustering of financial time series," Statistical Papers, Springer, vol. 58(3), pages 641-657, September.
    6. Plat, Richard, 2009. "Stochastic portfolio specific mortality and the quantification of mortality basis risk," Insurance: Mathematics and Economics, Elsevier, vol. 45(1), pages 123-132, August.
    7. Kondylis, Athanassios & Whittaker, Joe, 2008. "Spectral preconditioning of Krylov spaces: Combining PLS and PC regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2588-2603, January.
    8. Bolívar, Fernando & Duran, Miguel A. & Lozano-Vivas, Ana, 2023. "Bank business models, size, and profitability," Finance Research Letters, Elsevier, vol. 53(C).
    9. Ouyang, Yaofu & Li, Peng, 2018. "On the nexus of financial development, economic growth, and energy consumption in China: New perspective from a GMM panel VAR approach," Energy Economics, Elsevier, vol. 71(C), pages 238-252.
    10. Paschalis Arvanitidis & Athina Economou & Christos Kollias, 2016. "Terrorism’s effects on social capital in European countries," Public Choice, Springer, vol. 169(3), pages 231-250, December.
    11. Rizvi, Syed Kumail Abbas & Rahat, Birjees & Naqvi, Bushra & Umar, Muhammad, 2024. "Revolutionizing finance: The synergy of fintech, digital adoption, and innovation," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    12. Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2024. "Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia," Resources, MDPI, vol. 13(3), pages 1-18, March.
    13. Weili Duan & Bin He & Daniel Nover & Guishan Yang & Wen Chen & Huifang Meng & Shan Zou & Chuanming Liu, 2016. "Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods," Sustainability, MDPI, vol. 8(2), pages 1-15, January.
    14. Adele Ravagnani & Fabrizio Lillo & Paola Deriu & Piero Mazzarisi & Francesca Medda & Antonio Russo, 2024. "Dimensionality reduction techniques to support insider trading detection," Papers 2403.00707, arXiv.org, revised May 2024.
    15. Cling, Jean-Pierre & Delecourt, Clément, 2022. "Interlinkages between the Sustainable Development Goals," World Development Perspectives, Elsevier, vol. 25(C).
    16. Hino, Hideitsu & Wakayama, Keigo & Murata, Noboru, 2013. "Entropy-based sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 105-114.
    17. Angelucci, Federica & Conforti, Piero, 2010. "Risk management and finance along value chains of Small Island Developing States. Evidence from the Caribbean and the Pacific," Food Policy, Elsevier, vol. 35(6), pages 565-575, December.
    18. Roopam Shukla & Ankit Agarwal & Kamna Sachdeva & Juergen Kurths & P. K. Joshi, 2019. "Climate change perception: an analysis of climate change and risk perceptions among farmer types of Indian Western Himalayas," Climatic Change, Springer, vol. 152(1), pages 103-119, January.
    19. Poskitt, D.S. & Sengarapillai, Arivalzahan, 2013. "Description length and dimensionality reduction in functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 98-113.
    20. Taner Akan & Tim Solle, 2022. "Do macroeconomic and financial governance matter? Evidence from Germany, 1950–2019," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(4), pages 993-1045, October.

    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:spr:jclass:v:35:y:2018:i:2:d:10.1007_s00357-018-9258-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.