Abstract
An important feature of the time series in the real world is that its distribution has different degrees of asymmetry, which is what we call irreversibility. In this paper, we propose a new method named permutation pattern (PP) to calculate the Kullback–Leibler divergence (\({D}_{\mathrm{KL}}\)) and the Jensen–Shannon divergence (\({D}_{\mathrm{JS}}\)) to explore the irreversibility of time series. Meanwhile, we improve \({D}_{\mathrm{JS}}\) and obtain a complete mean divergence (\({D}_{\mathrm{m}}\)) through averaging \({D}_{\mathrm{KL}}\) of a time series and its inverse time series. The variation trend of \({D}_{\mathrm{m}}\) is similar to \({D}_{\mathrm{JS}}\), but the value of \({D}_{\mathrm{m}}\) is slightly larger and the description of irreversibility is more intuitive. Furthermore, we compare \({D}_{\mathrm{JS}}\) and \({D}_{\mathrm{m}}\) calculated by PP with those calculated by the horizontal visibility graph, and discuss their respective characteristics. Then, we investigate the advantages of \(\mathrm{D}_{\mathrm{JS}}\) and \({D}_{\mathrm{m}}\) through length variation, dynamic time variation, multiscale and so on. It is worth mentioning that we introduce Score and variance to analyze the practical significance of stock irreversibility.
Similar content being viewed by others
References
Mauboussin, M.J.: Revisiting market efficiency: the stock market as a complex adaptive system. J. Appl. Corp. Finance 14, 47–55 (2002)
Shi, W.B., Shang, P.J.: Cross-sample entropy statistic as a measure of synchronism and cross-correlation of stock markets. Nonlinear Dyn. 71, 539–554 (2013)
Xiao, D., Wang, J.: Modeling stock price dynamics by continuum percolation system and relevant complex systems analysis. Physica A 391, 4827–4838 (2012)
Hu, Q.Z., Lu, H.P., Yu, X.X., Cheng, N.: Compressive measurement of urban public traffic system based on relational entropy and complex matter element. Syst. Eng. Theory Pract. 31, 186–192 (2011)
Berkes, F.: From community-based resource management to complex systems: the scale issue and marine commons. Ecol. Soc. 11, 709–723 (2006)
Phillips, C.O., Singa, R.M., Rubin, H.R., Jaarsma, T.: Complexity of program and clinical outcomes of heart failure disease management incorporating specialist nurse-led heart failure clinics. A meta-regression analysis. Eur. J. Heart Fail. 7, 333–341 (2014)
Mahmoud, G.M., Aly, S.A., AlKashif, M.A.: Dynamical properties and chaos synchronization of a new chaotic complex nonlinear system. Nonlinear Dyn. 51, 171–181 (2009)
Aoki, T., Yawata, K., Aoyagi, T.: Self-organization of complex networks as a dynamical system. Phys. Rev. E 91, 012908 (2015)
Bär, K.J., Boettger, M.K., et al.: Non-linear complexity measures of heart rate variability in acute schizophrenia. J. Clin. Neurophysiol. 118, 2009–2015 (2007)
Grewal, M.S., Glover, K.: Identifiability of linear and nonlinear dynamical systems. IEEE Trans. Automat. Contr. 21, 833–837 (1976)
Das, A.N., Murthy, R., Dan, O.P., Stephanou, H.E.: A multiscale assembly and packaging system for manufacturing of complex micro-nano devices. IEEE Trans. Autom. Sci. Eng. 9, 160–170 (2011)
Xu, M., Shang, P.J.: Multiscale time irreversibility analysis of financial time series based on segmentation. Nonlinear Dyn. 94, 1603–1618 (2018)
Ramsey, J.B., Rothman, P.: Time irreversibility and business cycle asymmetry. Working Papers 28, pp. 1–21 (1993)
Rothman, P.: The comparative power of the TR test against simple threshold models. J. Appl. Econ. 7, 187–195 (2010)
Hinich, M., Rothman, P.: Frequency-domain test of time reversibility. Macroecon Dyn. 2, 72–78 (1998)
Costa, M.D., Goldberger, A.L., Peng, C.K.: Broken asymmetry of the human heartbeat loss of time irreversibility in aging and disease. Phy. Rew. Lett. 95, 198102 (2005)
Costa, M.D., Peng, C.K., Goldberger, A.L.: Multiscale analysis of heart rate dynamics-entropy and time irreversibility measures. Cardiovasc. Eng. 8, 88–93 (2008)
Porta, A., Guzzetti, S., Montano, N., Gnecchi-Ruscone, T., Furlan, R., Malliani, A.: Time reversibility in short-term heart period variability. Comput. Cardiol. 33, 77–80 (2006)
Guzik, P., Piskorski, J., Krauze, T., Wykretowicz, A., Wysocki, H.: Heart rate asymmetry by Poincaré plots of RR intervals. Biomed. Eng Biomed. Tech. 51, 272–275 (2006)
Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: the visibility graph. Proc. Natl. Acad. Sci. USA 105, 4972–4975 (2008)
Luque, B., Lacasa, L., Luque, J., Ballesteros, F.: Horizontal visibility graphs: exact results for random time series. Phy. Rev. E 80, 046103 (2009)
Lacasa, L., Luque, B., Luque, J., Nuno, J.C.: The visibility graph: a new method for estimating the Hurst exponent of fractional Brownian motion. EPL Europhys. Lett. 86, 30001 (2009)
Lacasa, L., Nuñez, A., et al.: Time series irreversibility: a visibility graph approach. Eur. Phys. J. B 85, 217–227 (2012)
Lacasa, L., Flanagan, R.: Time reversibility from visibility graphs of non-stationary processes. Phys. Rev. E 92, 022817 (2015)
Shannon, C.E.: The Mathematical Theory of Communication, vol. 5, pp. 3–55. McGraw-Hill, New York (1948)
Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951)
Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing, vol. 26, pp. 91–92. MIT Press, Cambridge (1999)
Dagan, I., Lee, L., Pereira, F.: Similarity-based methods for word sense disambiguation. ACL 6493, 5663 (1997)
Bandt, C., Pompe, B.: Permutation entropy: a natural complexity measure for time series. IEEE. Trans. Inf. Theory 21, 493–501 (1975)
Cánovas, J.S., Guillamón, A.: Permutations and time series analysis. Chaos 19, 043103 (2009)
Matilla-García, M., Marín, M.R.: A non-parametric independence test using permutation entropy. J. Econom. 144, 139–155 (2008)
Amigó, J.M., Zambrano, S., Sanjuán, M.A.F.: True and false forbidden patterns in deterministic and random dynamics. Europhys. Lett. 79, 50001 (2007)
Wu, S.D., Wu, C.W., et al.: Analysis of complex time series using refined composite multiscale entropy. Phys. Lett. A 378, 1369–1374 (2014)
Hosking, J.R.M.: Fractional differencing. Biometrika 68, 165–176 (1981)
Podobnik, B., Horvatic, D., et al.: Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes. Physica A 387, 3954–3959 (2008)
Granger, C.W.J., Joyeux, R.: An Introduction to Long-Memory Time Series Models and Fractional Differencing. Harvard University Press, Cambridge (2001)
Acknowledgements
The financial support from the Fundamental Research Funds for the Central Universities (2018YJS170) is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, J., Shang, P. & Zhang, X. Time series irreversibility analysis using Jensen–Shannon divergence calculated by permutation pattern. Nonlinear Dyn 96, 2637–2652 (2019). https://doi.org/10.1007/s11071-019-04950-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11071-019-04950-6