American Institute of Mathematical Sciences

Advanced
December  2015, 20(10): 3507-3524. doi: 10.3934/dcdsb.2015.20.3507

Entropy determination based on the ordinal structure of a dynamical system

Karsten Keller 1, , Sergiy Maksymenko 2, and  Inga Stolz 3,

1. 

Universität zu Lübeck, Institut für Mathematik, Ratzeburger Allee 160, 23562 Lübeck
2. 

Institute of Mathematics of NAS of Ukraine, Tereshchenkivs'ka str., 3, 01601 Kyiv
3. 

Universität zu Lübeck, Institut für Mathematik, Ratzeburger Allee 160, Lübeck, 23562, Germany

Received  December 2014 Revised  February 2015 Published  September 2015

The ordinal approach to evaluate time series due to innovative works of Bandt and Pompe has increasingly established itself among other techniques of nonlinear time series analysis. In this paper, we summarize and generalize the theory of determining the Kolmogorov-Sinai entropy of a measure-preserving dynamical system via increasing sequences of order generated partitions of the state space. Our main focus are measuring processes without information loss. Particularly, we consider the question of the minimal necessary number of measurements related to the properties of a given dynamical system.
Keywords: Kolmogorov-Sinai entropy, algebra reconstruction dimension., permutation entropy, ordinal time series analysis.
Mathematics Subject Classification: Primary: 37A35, 28D05; Secondary: 37M1.
Citation: Karsten Keller, Sergiy Maksymenko, Inga Stolz. Entropy determination based on the ordinal structure of a dynamical system. Discrete & Continuous Dynamical Systems - B, 2015, 20 (10) : 3507-3524. doi: 10.3934/dcdsb.2015.20.3507
References:
[1]

J. M. Amigó, K. Keller and V. A. Unakafova, Ordinal symbolic analysis and its application to biomedical recordings,, Philosophical Transactions Royal Society A, 373 (2015). Google Scholar

[2]

J. M. Amigó, K. Keller and J. Kurths (eds.), Recent progess in symbolic dynamics and permutation complexity. Ten years of permutation entropy,, The European Physical Journal Special Topics, 222 (2013), 241. Google Scholar

[3]

J. M. Amigó, The equality of Kolmogorov-Sinai entropy and metric permutation entropy generalized,, Physica D: Nonlinear Phenomena, 241 (2012), 789. doi: 10.1016/j.physd.2012.01.004. Google Scholar

[4]

J. M. Amigó, Permutation Complexity in Dynamical Systems: Ordinal Patterns, Permutation Entropy and All That,, Springer Series in Synergetics, (2010). doi: 10.1007/978-3-642-04084-9. Google Scholar

[5]

J. M. Amigó, M. B. Kennel and L. Kocarev, The permutation entropy rate equals the metric entropy rate for ergodic information sources and ergodic dynamical systems,, Physica D: Nonlinear Phenomena, 210 (2005), 77. doi: 10.1016/j.physd.2005.07.006. Google Scholar

[6]

A. Antoniouk, K. Keller and S. Maksymenko, Kolmogorov-Sinai entropy via separation properties of order-generated sigma-algebras,, Discrete and Continuous Dynamical Systems - A, 34 (2014), 1793. Google Scholar

[7]

C. Bandt, G. Keller and B. Pompe, Entropy of interval maps via permutations,, Nonlinearity, 15 (2002), 1595. doi: 10.1088/0951-7715/15/5/312. Google Scholar

[8]

C. Bandt and B. Pompe, Permutation entropy: A natural complexity measure for time series,, Physical Review Letters, 88 (2002). Google Scholar

[9]

M. Einsiedler, E. Lindenstrauss and T. Ward, Entropy in ergodic theory and homogeneous dynamics,, unpublished, (2014). Google Scholar

[10]

M. Einsiedler and T. Ward, Ergodic Theory With a View Towards Number Theory,, Graduate Texts in Mathematics, (2010). Google Scholar

[11]

W. Hurewicz and H. Wallman, Dimension Theory,, Princeton Mathematical Series, (1941). Google Scholar

[12]

A. S. Kechris, Classical Descriptive Set Theory,, Graduate Texts in Mathematics, (1995). doi: 10.1007/978-1-4612-4190-4. Google Scholar

[13]

K. Keller, Permutations and the Kolmogorov-Sinai entropy,, Discrete and Continuous Dynamical Systems - A, 32 (2012), 891. doi: 10.3934/dcds.2012.32.891. Google Scholar

[14]

K. Keller and M. Sinn, Kolmogorov-Sinai entropy from the ordinal viewpoint,, Physica D: Nonlinear Phenomena, 239 (2010), 997. doi: 10.1016/j.physd.2010.02.006. Google Scholar

[15]

K. Keller and M. Sinn, A standardized approach to the Kolmogorov-Sinai entropy,, Nonlinearity, 22 (2009), 2417. doi: 10.1088/0951-7715/22/10/006. Google Scholar

[16]

K. Keller, M. Sinn and J. Emonds, Time series from the ordinal viewpoint,, Stochastics and Dynamics, 7 (2007), 247. doi: 10.1142/S0219493707002025. Google Scholar

[17]

K. Keller, A. M. Unakafov and V. A. Unakafova, On the relation of KS entropy and permutation entropy,, Physica D: Nonlinear Phenomena, 241 (2012), 1477. doi: 10.1016/j.physd.2012.05.010. Google Scholar

[18]

W. Krieger, On entropy and generators of measure-preserving transformations,, Transactions of the American Mathematical Society, 149 (1970), 453. doi: 10.1090/S0002-9947-1970-0259068-3. Google Scholar

[19]

W. Parry, Generators and strong generators in ergodic theory,, Bulletin of the American Mathematical Society, 72 (1966), 294. doi: 10.1090/S0002-9904-1966-11498-2. Google Scholar

[20]

M. Riedl, A. Müller and N. Wessel, Practical considerations of permutation entropy; a tutorial review,, The European Physical Journal Special Topics, 222 (2013), 249. Google Scholar

[21]

V. A. Rohlin, Exact endomorphisms of a Lebesgue space,, Izvestiya Akademii Nauk SSSR Seriya Matematicheskaya, 25 (1961), 499. Google Scholar

[22]

T. Sauer, J. A. Yorke and M. Casdagli, Embeddology,, Journal of Statistical Physics, 65 (1991), 579. doi: 10.1007/BF01053745. Google Scholar

[23]

F. Takens, Detecting strange attractors in turbulence,, in Dynamical Systems and Turbulence (eds. D. A. Rand and L. S. Young), (1981), 366. Google Scholar

[24]

A. M. Unakafov and K. Keller, Conditional entropy of ordinal patterns,, Physica D: Nonlinear Phenomena, 269 (2014), 94. doi: 10.1016/j.physd.2013.11.015. Google Scholar

[25]

V. A. Unakafova and K. Keller, Efficiently measuring complexity on the basis of real-world data,, Entropy, 15 (2013), 4392. doi: 10.3390/e15104392. Google Scholar

[26]

V. A. Unakafova, A. M. Unakafov and K. Keller, An approach to comparing Kolmogorov-Sinai and permutation entropy,, The European Physical Journal Special Topics, 222 (2013), 353. doi: 10.1140/epjst/e2013-01846-7. Google Scholar

[27]

P. Walters, An Introduction to Ergodic Theory,, Graduate Texts in Mathematics, (2000). doi: 10.1007/978-1-4612-5775-2. Google Scholar

[28]

M. Zanin, L. Zunino, O. A. Rosso and D. Papo, Permutation entropy and its main biomedical and econophysics applications: A review,, Entropy, 14 (2012), 1553. doi: 10.3390/e14081553. Google Scholar

show all references

References:
[1]

J. M. Amigó, K. Keller and V. A. Unakafova, Ordinal symbolic analysis and its application to biomedical recordings,, Philosophical Transactions Royal Society A, 373 (2015). Google Scholar

[2]

J. M. Amigó, K. Keller and J. Kurths (eds.), Recent progess in symbolic dynamics and permutation complexity. Ten years of permutation entropy,, The European Physical Journal Special Topics, 222 (2013), 241. Google Scholar

[3]

J. M. Amigó, The equality of Kolmogorov-Sinai entropy and metric permutation entropy generalized,, Physica D: Nonlinear Phenomena, 241 (2012), 789. doi: 10.1016/j.physd.2012.01.004. Google Scholar

[4]

J. M. Amigó, Permutation Complexity in Dynamical Systems: Ordinal Patterns, Permutation Entropy and All That,, Springer Series in Synergetics, (2010). doi: 10.1007/978-3-642-04084-9. Google Scholar

[5]

J. M. Amigó, M. B. Kennel and L. Kocarev, The permutation entropy rate equals the metric entropy rate for ergodic information sources and ergodic dynamical systems,, Physica D: Nonlinear Phenomena, 210 (2005), 77. doi: 10.1016/j.physd.2005.07.006. Google Scholar

[6]

A. Antoniouk, K. Keller and S. Maksymenko, Kolmogorov-Sinai entropy via separation properties of order-generated sigma-algebras,, Discrete and Continuous Dynamical Systems - A, 34 (2014), 1793. Google Scholar

[7]

C. Bandt, G. Keller and B. Pompe, Entropy of interval maps via permutations,, Nonlinearity, 15 (2002), 1595. doi: 10.1088/0951-7715/15/5/312. Google Scholar

[8]

C. Bandt and B. Pompe, Permutation entropy: A natural complexity measure for time series,, Physical Review Letters, 88 (2002). Google Scholar

[9]

M. Einsiedler, E. Lindenstrauss and T. Ward, Entropy in ergodic theory and homogeneous dynamics,, unpublished, (2014). Google Scholar

[10]

M. Einsiedler and T. Ward, Ergodic Theory With a View Towards Number Theory,, Graduate Texts in Mathematics, (2010). Google Scholar

[11]

W. Hurewicz and H. Wallman, Dimension Theory,, Princeton Mathematical Series, (1941). Google Scholar

[12]

A. S. Kechris, Classical Descriptive Set Theory,, Graduate Texts in Mathematics, (1995). doi: 10.1007/978-1-4612-4190-4. Google Scholar

[13]

K. Keller, Permutations and the Kolmogorov-Sinai entropy,, Discrete and Continuous Dynamical Systems - A, 32 (2012), 891. doi: 10.3934/dcds.2012.32.891. Google Scholar

[14]

K. Keller and M. Sinn, Kolmogorov-Sinai entropy from the ordinal viewpoint,, Physica D: Nonlinear Phenomena, 239 (2010), 997. doi: 10.1016/j.physd.2010.02.006. Google Scholar

[15]

K. Keller and M. Sinn, A standardized approach to the Kolmogorov-Sinai entropy,, Nonlinearity, 22 (2009), 2417. doi: 10.1088/0951-7715/22/10/006. Google Scholar

[16]

K. Keller, M. Sinn and J. Emonds, Time series from the ordinal viewpoint,, Stochastics and Dynamics, 7 (2007), 247. doi: 10.1142/S0219493707002025. Google Scholar

[17]

K. Keller, A. M. Unakafov and V. A. Unakafova, On the relation of KS entropy and permutation entropy,, Physica D: Nonlinear Phenomena, 241 (2012), 1477. doi: 10.1016/j.physd.2012.05.010. Google Scholar

[18]

W. Krieger, On entropy and generators of measure-preserving transformations,, Transactions of the American Mathematical Society, 149 (1970), 453. doi: 10.1090/S0002-9947-1970-0259068-3. Google Scholar

[19]

W. Parry, Generators and strong generators in ergodic theory,, Bulletin of the American Mathematical Society, 72 (1966), 294. doi: 10.1090/S0002-9904-1966-11498-2. Google Scholar

[20]

M. Riedl, A. Müller and N. Wessel, Practical considerations of permutation entropy; a tutorial review,, The European Physical Journal Special Topics, 222 (2013), 249. Google Scholar

[21]

V. A. Rohlin, Exact endomorphisms of a Lebesgue space,, Izvestiya Akademii Nauk SSSR Seriya Matematicheskaya, 25 (1961), 499. Google Scholar

[22]

T. Sauer, J. A. Yorke and M. Casdagli, Embeddology,, Journal of Statistical Physics, 65 (1991), 579. doi: 10.1007/BF01053745. Google Scholar

[23]

F. Takens, Detecting strange attractors in turbulence,, in Dynamical Systems and Turbulence (eds. D. A. Rand and L. S. Young), (1981), 366. Google Scholar

[24]

A. M. Unakafov and K. Keller, Conditional entropy of ordinal patterns,, Physica D: Nonlinear Phenomena, 269 (2014), 94. doi: 10.1016/j.physd.2013.11.015. Google Scholar

[25]

V. A. Unakafova and K. Keller, Efficiently measuring complexity on the basis of real-world data,, Entropy, 15 (2013), 4392. doi: 10.3390/e15104392. Google Scholar

[26]

V. A. Unakafova, A. M. Unakafov and K. Keller, An approach to comparing Kolmogorov-Sinai and permutation entropy,, The European Physical Journal Special Topics, 222 (2013), 353. doi: 10.1140/epjst/e2013-01846-7. Google Scholar

[27]

P. Walters, An Introduction to Ergodic Theory,, Graduate Texts in Mathematics, (2000). doi: 10.1007/978-1-4612-5775-2. Google Scholar

[28]

M. Zanin, L. Zunino, O. A. Rosso and D. Papo, Permutation entropy and its main biomedical and econophysics applications: A review,, Entropy, 14 (2012), 1553. doi: 10.3390/e14081553. Google Scholar

[1]

Karsten Keller. Permutations and the Kolmogorov-Sinai entropy. Discrete & Continuous Dynamical Systems - A, 2012, 32 (3) : 891-900. doi: 10.3934/dcds.2012.32.891
[2]

Tim Gutjahr, Karsten Keller. Equality of Kolmogorov-Sinai and permutation entropy for one-dimensional maps consisting of countably many monotone parts. Discrete & Continuous Dynamical Systems - A, 2019, 39 (7) : 4207-4224. doi: 10.3934/dcds.2019170
[3]

Alexandra Antoniouk, Karsten Keller, Sergiy Maksymenko. Kolmogorov-Sinai entropy via separation properties of order-generated $\sigma$-algebras. Discrete & Continuous Dynamical Systems - A, 2014, 34 (5) : 1793-1809. doi: 10.3934/dcds.2014.34.1793
[4]

Yixiao Qiao, Xiaoyao Zhou. Zero sequence entropy and entropy dimension. Discrete & Continuous Dynamical Systems - A, 2017, 37 (1) : 435-448. doi: 10.3934/dcds.2017018
[5]

Min Qian, Jian-Sheng Xie. Entropy formula for endomorphisms: Relations between entropy, exponents and dimension. Discrete & Continuous Dynamical Systems - A, 2008, 21 (2) : 367-392. doi: 10.3934/dcds.2008.21.367
[6]

Xiaomin Zhou. Relative entropy dimension of topological dynamical systems. Discrete & Continuous Dynamical Systems - A, 2019, 39 (11) : 6631-6642. doi: 10.3934/dcds.2019288
[7]

Yong Yang, Bingsheng Zhang. On the Kolmogorov entropy of the weak global attractor of 3D Navier-Stokes equations:Ⅰ. Discrete & Continuous Dynamical Systems - B, 2017, 22 (6) : 2339-2350. doi: 10.3934/dcdsb.2017101
[8]

Xiao-Qian Jiang, Lun-Chuan Zhang. Stock price fluctuation prediction method based on time series analysis. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 915-927. doi: 10.3934/dcdss.2019061
[9]

Michael Brandenbursky, Michał Marcinkowski. Entropy and quasimorphisms. Journal of Modern Dynamics, 2019, 15: 143-163. doi: 10.3934/jmd.2019017
[10]

Wenxiang Sun, Cheng Zhang. Zero entropy versus infinite entropy. Discrete & Continuous Dynamical Systems - A, 2011, 30 (4) : 1237-1242. doi: 10.3934/dcds.2011.30.1237
[11]

Jean Dolbeault, Giuseppe Toscani. Fast diffusion equations: Matching large time asymptotics by relative entropy methods. Kinetic & Related Models, 2011, 4 (3) : 701-716. doi: 10.3934/krm.2011.4.701
[12]

María Anguiano, Alain Haraux. The $\varepsilon$-entropy of some infinite dimensional compact ellipsoids and fractal dimension of attractors. Evolution Equations & Control Theory, 2017, 6 (3) : 345-356. doi: 10.3934/eect.2017018
[13]

Annalisa Pascarella, Alberto Sorrentino, Cristina Campi, Michele Piana. Particle filtering, beamforming and multiple signal classification for the analysis of magnetoencephalography time series: a comparison of algorithms. Inverse Problems & Imaging, 2010, 4 (1) : 169-190. doi: 10.3934/ipi.2010.4.169
[14]

José M. Amigó, Karsten Keller, Valentina A. Unakafova. On entropy, entropy-like quantities, and applications. Discrete & Continuous Dynamical Systems - B, 2015, 20 (10) : 3301-3343. doi: 10.3934/dcdsb.2015.20.3301
[15]

Ping Huang, Ercai Chen, Chenwei Wang. Entropy formulae of conditional entropy in mean metrics. Discrete & Continuous Dynamical Systems - A, 2018, 38 (10) : 5129-5144. doi: 10.3934/dcds.2018226
[16]

François Blanchard, Wen Huang. Entropy sets, weakly mixing sets and entropy capacity. Discrete & Continuous Dynamical Systems - A, 2008, 20 (2) : 275-311. doi: 10.3934/dcds.2008.20.275
[17]

Boris Kruglikov, Martin Rypdal. Entropy via multiplicity. Discrete & Continuous Dynamical Systems - A, 2006, 16 (2) : 395-410. doi: 10.3934/dcds.2006.16.395
[18]

Nicolas Bedaride. Entropy of polyhedral billiard. Discrete & Continuous Dynamical Systems - A, 2007, 19 (1) : 89-102. doi: 10.3934/dcds.2007.19.89
[19]

Baolin He. Entropy of diffeomorphisms of line. Discrete & Continuous Dynamical Systems - A, 2017, 37 (9) : 4753-4766. doi: 10.3934/dcds.2017204
[20]

Lluís Alsedà, David Juher, Deborah M. King, Francesc Mañosas. Maximizing entropy of cycles on trees. Discrete & Continuous Dynamical Systems - A, 2013, 33 (8) : 3237-3276. doi: 10.3934/dcds.2013.33.3237

2018 Impact Factor: 1.008

Tools

Metrics

  • PDF downloads (10)
  • HTML views (0)
  • Cited by (5)

Other articles
by authors

[Back to Top]

Copyright © 2019 American Institute of Mathematical Sciences