Similarity Analysis Based on Bose-Einstein Divergences for Financial Time Series

  • Ryszard Szupiluk
  • Tomasz Ząbkowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7824)


Similarity assessment between financial time series is one of problems where the proper methodological choice is very important. The typical correlation approach can lead to misleading results. Often the similarity measure is opposite to the visual observations, expert’s knowledge and even a common sense. The reasons of that can be associated with the properties of the correlation measure and its adequateness for analyzed data, as well as in terms of methodological aspects. In this article, we indicate disadvantages associated with the use of correlation to assess the similarity of financial time series and propose an alternative solution based on divergence measures. In particular, we focus on the Bose-Einstein divergence. The practical experiments conducted on simulated and real data confirmed our concept.


time series similarity divergence measures Bose-Einstein divergence 


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  1. 1.
    Amari, S.: Diferential-Geometrical Methods in Statistics. Springer (1985)Google Scholar
  2. 2.
    Anscombe, F.J.: Graphs in statistical analysis. The American Statistician 27, 17–21 (1973)Google Scholar
  3. 3.
    Bashashati, A., Fatourechi, M., Ward, R., Birch, G.: A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. Journal of Neural Engineering 4, 32–57 (2007)CrossRefGoogle Scholar
  4. 4.
    Cardoso, J.-F., Comon, P.: Independent component analysis, a survey of some algebraic methods. In: Proc. ISCAS Conference Atlanta, vol. 2, pp. 93–96 (1996)Google Scholar
  5. 5.
    Cichocki, A., Zdunek, R., Amari, S.-i.: Csiszár’s Divergences for Non-negative Matrix Factorization: Family of New Algorithms. In: Rosca, J.P., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 32–39. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Cichocki, A., Zdunek, R., Phan, A.-H., Amari, S.: Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis. John Wiley (2009)Google Scholar
  7. 7.
    Csiszar, I.: Information measures: A critical survey. In: Prague Conference on Information Theory, vol. A, pp. 73–86. Academia Prague (1974)Google Scholar
  8. 8.
    Krutsinger, J.: Trading Systems: Secrets of the Masters. McGraw-Hill (1997)Google Scholar
  9. 9.
    Luo, Y., Davis, D., Liu, K.: A Multi-Agent Decision Support System for Stock Trading. The IEEE Network Magazine Special Issue on Enterprise Networking and Services 16(1) (2002)Google Scholar
  10. 10.
    Rodgers, J.L., Nicewander, W.A.: Thirteen ways to look at the correlation coefficient. The American Statistician 42(1), 59–66 (1988)CrossRefGoogle Scholar
  11. 11.
    Samorodnitskij, G., Taqqu, M.: Stable non-Gaussian random processes: stochastic models with infinitive variance. Chapman and Hall, New York (1994)Google Scholar
  12. 12.
    Therrien, C.W.: Discrete Random Signals and Statistical Signal Processing. Prentice Hall, New Jersey (1992)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ryszard Szupiluk
    • 1
  • Tomasz Ząbkowski
    • 2
  1. 1.Warsaw School of EconomicsWarsawPoland
  2. 2.Warsaw University of Life SciencesWarsawPoland

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