LettersNo Access

USING PERMUTATIONS TO FIND STRUCTURAL CHANGES IN TIME SERIES

Universidad Politécnica de Cartagena, Departamento de Matemática Aplicada y Estadística, C/Doctor Fleming sn. 30202 Cartagena, Spain

Search for more papers by this author

A. GUILLAMÓN

Universidad Politécnica de Cartagena, Departamento de Matemática Aplicada y Estadística, C/Doctor Fleming sn. 30202 Cartagena, Spain

Search for more papers by this author

, and

M. C. RUIZ

Universidad Politécnica de Cartagena, Departamento de Matemática Aplicada y Estadística, C/Doctor Fleming sn. 30202 Cartagena, Spain

Search for more papers by this author

https://doi.org/10.1142/S0219477511000375Cited by:5 (Source: Crossref)

Abstract

The number of permutations appearing in data series is used for detecting changes in the structure of such series. We show the influence of the permutations length (embedding dimension) in order to get good results. We use permutations to analyze real data from medical and biological origins. Some problems that appear in applying these techniques are pointed out.

Keywords:

References

L. Alsedá , J. Llibre and M. Misiurewicz , Combinatorial Dynamics and Entropy in Dimension One ( World Scientific Publishing , 1993 ) . Link, Google Scholar
J. M. Amigó, S. Elizalde and M. B. Kennel, J. Combin. Theor. Ser. A 115, 485 (2008). Crossref, Web of Science, Google Scholar
C. Bandt and B. Pompe, Phys. Rev. Lett. 88, 174102 (2002), DOI: 10.1103/PhysRevLett.88.174102. Crossref, Web of Science, Google Scholar
C. Bandt, G. Keller and B. Pompe, Nonlinearity 15, 1595 (2002), DOI: 10.1088/0951-7715/15/5/312. Crossref, Web of Science, Google Scholar
L. S. Block and W. A. Coppel, Dynamics in one dimension, Lecture Notes in Math., Vol. 1513 (Springer, Berlin, 1992) . Google Scholar
J. Bruhn, H. Ropcke and A. Hoeft, Anesthesiology 92, 715 (2000), DOI: 10.1097/00000542-200003000-00016. Crossref, Web of Science, Google Scholar
A. A. Bruzzoet al., Neurol. Sci. 29, 3 (2008), DOI: 10.1007/s10072-008-0851-3. Crossref, Web of Science, Google Scholar
R. Bowen, Trans. Amer. Math. Soc. 153, 401 (1971), DOI: 10.1090/S0002-9947-1971-0274707-X. Crossref, Web of Science, Google Scholar
J. S. Cánovas , Int. J. Comput. Math. . Google Scholar
Y. H. Caoet al., Phys. Rev. E 70, 046217 (2004), DOI: 10.1103/PhysRevE.70.046217. Crossref, Web of Science, Google Scholar
J. D. Gibbons and S. Chakrabortis , Nonparametric Statistical Inference , 3rd edn. ( Marcel Dekker , New York , 1992 ) . Google Scholar
M. Matilla and M. Ruiz, J. Econometrics 144, 139 (2008). Crossref, Web of Science, Google Scholar
S. McClellan and J. Gibson, IEEE Int. Conf. Acoustics, Speech Sign. Processing 1, 201 (1994). Google Scholar
E. Olofsen, J. W. Sleigh and A. Dahan, Br. J. Anaesth. (2008), DOI: 10.1093/bja/aen290. Google Scholar
R. Quian-Quirogaet al., Electroencephalography and Clinical Neurophysiology 103, 434 (1997). Crossref, Google Scholar
J. Roca–González et al. , Effects of propofol anesthesia of nonlineal properties of EEG: Lyapunov exponents and short–term predictability , 4th European Conf. of the Int. Federation for Medical and Biological Engineering, ECIFMBE 2008 ( 2008 ) . Google Scholar
www.ece.ucsb.edu/Faculty/Rabiner/ece259/speech%20recognition%20course.html . Google Scholar
W. Xu, Q. Ding and B. X. Wang, Acoust. Sci. Technol. 25, 54 (2004), DOI: 10.1250/ast.25.54. Crossref, Web of Science, Google Scholar