Similarity Analysis Based on Bose-Einstein Divergences for Financial Time Series
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.
Keywordstime series similarity divergence measures Bose-Einstein divergence
Unable to display preview. Download preview PDF.
- 1.Amari, S.: Diferential-Geometrical Methods in Statistics. Springer (1985)Google Scholar
- 2.Anscombe, F.J.: Graphs in statistical analysis. The American Statistician 27, 17–21 (1973)Google Scholar
- 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
- 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.Csiszar, I.: Information measures: A critical survey. In: Prague Conference on Information Theory, vol. A, pp. 73–86. Academia Prague (1974)Google Scholar
- 8.Krutsinger, J.: Trading Systems: Secrets of the Masters. McGraw-Hill (1997)Google Scholar
- 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
- 11.Samorodnitskij, G., Taqqu, M.: Stable non-Gaussian random processes: stochastic models with infinitive variance. Chapman and Hall, New York (1994)Google Scholar
- 12.Therrien, C.W.: Discrete Random Signals and Statistical Signal Processing. Prentice Hall, New Jersey (1992)Google Scholar