1. DSpace at KDPU
  2. Електронні публікації
  3. Фізико-математичний факультет
  4. Кафедра інформатики та прикладної математики
Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал: http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/7003
Назва: Identifying stock market crashes by fuzzy measures of complexity
Автори: Bielinskyi, Andrii
Соловйов, Володимир Миколайович
Семеріков, Сергій Олексійович
Solovieva, Viktoria
Білінський, Андрій Іванович
Соловйова, Вікторія Володимирівна
Ключові слова: crash
critical event
stock market
entropy
recurrence plot
fuzzy set theory
indicator-precursor of crisis phenomena
fuzzy measure of complexity
Дата публікації: 13-гру-2021
Видавництво: Kyiv National Economic University named after Vadym Hetman
Бібліографічний опис: Bielinskyi A. Identifying stock market crashes by fuzzy measures of complexity / Andrii Bielinskyi, Vladimir Soloviev, Serhiy Semerikov, Viktoria Solovieva // Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi. – 2021. – Vol. 10. – P. 3-45. DOI : 10.33111/nfmte.2021.003
Короткий огляд (реферат): This study, for the first time, presents the possibility of using fuzzy set theory in combination with information theory and recurrent analysis to construct indicators (indicators-precursors) of crisis phenomena in complex nonlinear systems. In our study, we analyze the 4 most important crisis periods in the history of the stock market – 1929, 1987, 2008 and the COVID-19 pandemic in 2020. In particular, using the sliding window procedure, we analyze how the complexity of the studied crashes changes over time, and how it depends on events such as the global stock market crises. For comparative analysis, we take classical Shannon entropy, approximation and permutation entropy, recurrent diagrams, and their fuzzy alternatives. Each of the fuzzy modifications uses three membership functions: exponential, sigmoidal, and simple linear functions. Empirical results demonstrate the fact that the fuzzification of classical entropy and recurrence approaches opens up prospects for constructing effective and reliable indicators-precursors of critical events in the studied complex systems
Опис: 1. Acharya, U.R., Sree, S.V., Chattopadhyay, S., Yu, W., & Ang, P.C.A. (2011). Application of recurrence quantification analysis for the automated identification of epileptic EEG signals. International journal of neural systems, 21(03), 199-211. https://doi.org/10.1142/s0129065711002808 2. Aldalou, E., & Perçin, S. (2020). Application of integrated fuzzy MCDM approach for financial performance evaluation of Turkish technology sector. International Journal of Procurement Management, 13(1), 1-23. https://doi.org/10.1504/ijpm.2020.105198 3. Al-Sharhan, S., Karray, F., Gueaieb, W., & Basir, O. (2001). Fuzzy entropy: a brief survey. In Proceedings of the 10th IEEE international conference on fuzzy systems (Cat. No. 01CH37297): Vol. 3 (pp. 1135-1139). IEEE. https://doi.org/10.1109/FUZZ.2001.1008855 4. Alves, P. (2019). Chaos in historical prices and volatilities with five- dimensional Euclidean spaces. Chaos, Solitons & Fractals: X, 1, Article 100002. https://doi.org/10.1016/j.csfx.2019.100002 5. Argyroudis, G. S., & Siokis, F. M. (2019). Spillover effects of Great Recession on Hong-Kong’s Real Estate Market: An analysis based on Causality Plane and Tsallis Curves of Complexity–Entropy. Physica A: Statistical Mechanics and its Applications, 524, 576–586. https://doi.org/10.1016/j.physa.2019.04.052 6. Azami, H., Fernández, A., & Escudero, J. (2017). Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis. Medical & Biological Engineering & Computing, 55(11), 2037–2052. https://doi.org/10.1007/s11517-017-1647-5 7. Bandt, C., & Pompe, B. (2002). Permutation Entropy: A Natural Complexity Measure for Time Series. Physical Review Letters, 88(17), Article 174102. https://doi.org/10.1103/physrevlett.88.174102 8. Bastos, J. A., & Caiado, J. (2011). Recurrence quantification analysis of global stock markets. Physica A: Statistical Mechanics and its Appli- cations, 390(7), 1315–1325. https://doi.org/10.1016/j.physa.2010.12.008 9. Bielinskyi, A., Semerikov, S., Serdyuk, O., Solovieva, V., Soloviev, V., & Pichl, L. (2020). Econophysics of sustainability indices. CEUR Workshop Proceedings, 2713, 372-392. http://ceur-ws.org/Vol- 2713/paper41.pdf 10. Bielinskyi, A., & Soloviev, V. (2018). Complex network precursors of crashes and critical events in the cryptocurrency market. CEUR Workshop Proceedings, 2292, 37-45. http://ceur-ws.org/Vol-2292/paper02.pdf 11. Bielinskyi, A., Soloviev, V., Semerikov, S., & Solovieva, V. (2019). Detecting stock crashes using Levy distribution. CEUR Workshop Proceedings, 2422, 420-433. http://ceur-ws.org/Vol-2422/paper34.pdf 12. Boccara, N. (2010). Modeling complex systems. Springer Science & Business Media. https://doi.org/10.1007/978-1-4419-6562-2 13. Castiglioni, P., & Di Rienzo, M. (2008). How the threshold “r” influences approximate entropy analysis of heart-rate variability. In Proceedings of the 2008 Computers in Cardiology (pp. 561-564). IEEE. https://doi.org/10.1109/CIC.2008.4749103 14. Chen, W., Wang, Z., Xie, H., & Yu, W. (2007). Characterization of Surface EMG Signal Based on Fuzzy Entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(2), 266–272. https://doi.org/10.1109/tnsre.2007.897025 15. Chen, W., Zhuang, J., Yu, W., & Wang, Z. (2009). Measuring complexity using FuzzyEn, ApEn, and SampEn. Medical Engineering & Physics, 31(1), 61–68. https://doi.org/10.1016/j.medengphy.2008.04.005 16. Collet, P., Eckmann, J. P., & Koch, H. (1981). Period doubling bifurcations for families of maps on R n . Journal of Statistical Physics, 25(1), 1–14. https://doi.org/10.1007/bf01008475 17. De Luca, A., & Termini, S. (1972). A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Information and Control, 20(4), 301–312. https://doi.org/10.1016/s0019-9958(72)90199-4 18. Duran, N.D., Dale, R., Kello, C.T., Street, C.N.H., & Richardson, D.C. (2013). Exploring the movement dynamics of deception. Frontiers in Psychology, 4, Article 140. https://doi.org/10.3389/fpsyg.2013.00140 19. Eckmann, J. P., & Ruelle, D. (1985). Ergodic theory of chaos and strange attractors. Reviews of Modern Physics, 57(3), 617–656. https://doi.org/10.1103/revmodphys.57.617 20. Eckmann, J. P., Kamphorst, S. O., & Ruelle, D. (1987). Recurrence Plots of Dynamical Systems. Europhysics Letters, 4(9), 973–977. https://doi.org/10.1209/0295-5075/4/9/004 21. Elias, J., & Narayanan Namboothiri, V. N. (2013). Cross-recurrence plot quantification analysis of input and output signals for the detection of chatter in turning. Nonlinear Dynamics, 76(1), 255–261. https://doi.org/10.1007/s11071-013-1124-0 22. Eroglu, D., McRobie, F. H., Ozken, I., Stemler, T., Wyrwoll, K. H., Breitenbach, S. F. M., Marwan, N., & Kurths, J. (2016). See–saw rela- tionship of the Holocene East Asian–Australian summer monsoon. Nature Communications, 7(1), Article 12929. https://doi.org/10.1038/ncomms12929 23. Farmer, J. D. (1982). Information Dimension and the Probabilistic Structure of Chaos. Zeitschrift Für Naturforschung A, 37(11), 1304–1326. https://doi.org/10.1515/zna-1982-1117 24. García-Ochoa, E., González-Sánchez, J., Acuña, N., & Euan, J. (2008). Analysis of the dynamics of Intergranular corrosion process of sensitised 304 stainless steel using recurrence plots. Journal of Applied Electrochemistry, 39(5), 637–645. https://doi.org/10.1007/s10800-008-9702-4 25. Gardini, L., Lupini, R., & Messia, M. G. (1989). Hopf bifurcation and transition to chaos in Lotka-Volterra equation. Journal of Mathematical Biology, 27(3), 259–272. https://doi.org/10.1007/bf00275811 26. GitHub. (2021). Complex systems measures. https://github.com/ Butman2099/Complex-systems-measures 27. GitHub. (2021). EntropyHub: An open-source toolkit for entropic time series analysis. https://github.com/MattWillFlood/EntropyHub 28. Graf, S. (1987). Statistically self-similar fractals. Probability Theory and Related Fields, 74(3), 357–392. https://doi.org/10.1007/bf00699096 29. Grassberger, P., & Procaccia, I. (1983). Characterization of Strange Attractors. Physical Review Letters, 50(5), 346–349. https://doi.org/10.1103/physrevlett.50.346 30. Grebogi, C., Ott, E., Pelikan, S., & Yorke, J. A. (1984). Strange attractors that are not chaotic. Physica D: Nonlinear Phenomena, 13(1-2), 261-268. https://doi.org/10.1016/0167-2789(84)90282-3 31. Harris, P., Litak, G., Iwaniec, J., & Bowen, C. R. (2016). Recurrence Plot and Recurrence Quantification of the Dynamic Properties of Cross- Shaped Laminated Energy Harvester. Applied Mechanics and Materials, 849, 95–105. https://doi.org/10.4028/www.scientific.net/amm.849.95 32. He, S., Sun, K., & Wang, R. (2018). Fractional fuzzy entropy algorithm and the complexity analysis for nonlinear time series. The European Physical Journal Special Topics, 227(7), 943-957. https://doi.org/10.1140/epjst/e2018-700098-x 33. Hou, Y., Aldrich, C., Lepkova, K., Machuca, L., & Kinsella, B. (2016). Monitoring of carbon steel corrosion by use of electrochemical noise and recurrence quantification analysis. Corrosion Science, 112, 63–72. https://doi.org/10.1016/j.corsci.2016.07.009 34. Humeau-Heurtier, A. (2015). The Multiscale Entropy Algorithm and Its Variants: A Review. Entropy, 17(5), 3110–3123. https://doi.org/10.3390/e17053110 35. Hutchinson, J. E. (1981). Fractals and Self Similarity. Indiana University Mathematics Journal, 30(5), 713-747. https://www.jstor.org/stable/24893080 36. Ishizaki, R., & Inoue, M. (2020). Analysis of local and global instability in foreign exchange rates using short-term information entropy. Physica A: Statistical Mechanics and its Applications, 555, Article 124595. https://doi.org/10.1016/j.physa.2020.124595 37. Iwaniec, J., Uhl, T., Staszewski, W. J., & Klepka, A. (2012). Detection of changes in cracked aluminium plate determinism by recurrence analysis. Nonlinear Dynamics, 70(1), 125–140. https://doi.org/10.1007/ s11071-012-0436-9 38. Jahanshahi, H., Yousefpour, A., Wei, Z., Alcaraz, R., & Bekiros, S. (2019). A financial hyperchaotic system with coexisting attractors: Dynamic investigation, entropy analysis, control and synchronization. Chaos, Solitons & Fractals, 126, 66–77. https://doi.org/10.1016/j.chaos.2019.05.023 39. Kantz, H. (1994). A robust method to estimate the maximal Lyapunov exponent of a time series. Physics Letters A, 185(1), 77–87. https://doi.org/10.1016/0375-9601(94)90991-1 40. Konvalinka, I., Xygalatas, D., Bulbulia, J., Schjødt, U., Jegindø, E. M., Wallot, S., van Orden, G., & Roepstorff, A. (2011). Synchronized arousal between performers and related spectators in a fire- walking ritual. Proceedings of the National Academy of Sciences of the United States of America, 108(20), 8514–8519. https://doi.org/10.1073/ pnas.1016955108 41. Lahmiri, S., & Bekiros, S. (2017). Disturbances and complexity in volatility time series. Chaos, Solitons & Fractals, 105, 38–42. https://doi.org/10.1016/j.chaos.2017.10.006 42. Lahmiri, S., & Bekiros, S. (2019). Nonlinear analysis of Casablanca Stock Exchange, Dow Jones and S&P500 industrial sectors with a comparison. Physica A: Statistical Mechanics and its Applications, 539, Article 122923. https://doi.org/10.1016/j.physa.2019.122923 43. Lahmiri, S., & Bekiros, S. (2020). Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic. Entropy, 22(8), Article 833. https://doi.org/10.3390/e22080833 44. Lahmiri, S., Bekiros, S., & Avdoulas, C. (2018). Time-dependent complexity measurement of causality in international equity markets: A spatial approach. Chaos, Solitons & Fractals, 116, 215–219. https://doi.org/10.1016/j.chaos.2018.09.030 45. Lahmiri, S., Uddin, G. S., & Bekiros, S. (2017). Nonlinear dynamics of equity, currency and commodity markets in the aftermath of the global financial crisis. Chaos, Solitons & Fractals, 103, 342–346. https://doi.org/10.1016/j.chaos.2017.06.019 46. Lam, W. S., Lam, W. H., Jaaman, S. H., & Liew, K. F. (2021). Performance Evaluation of Construction Companies Using Integrated Entropy–Fuzzy VIKOR Model. Entropy, 23(3), Article 320. https://doi.org/10.3390/e23030320 47. Li, P., Liu, C., Li, K., Zheng, D., Liu, C., & Hou, Y. (2014). Assessing the complexity of short-term heartbeat interval series by distribution entropy. Medical & Biological Engineering & Computing, 53(1), 77–87. https://doi.org/10.1007/s11517-014-1216-0 48. Li, S., Zhao, Z., Wang, Y., & Wang, Y. (2011). Identifying spatial patterns of synchronization between NDVI and climatic determinants using joint recurrence plots. Environmental Earth Sciences, 64(3), 851–859. https://doi.org/10.1007/s12665-011-0909-z 49. List of stock market crashes and bear markets. (2021, August 1). In Wikipedia. https://en.wikipedia.org/w/index.php?title=List_of_stock_ market_crashes_and_bear_markets 50. Longwic, R., Litak, G., & Sen, A. K. (2009). Recurrence Plots for Diesel Engine Variability Tests. Zeitschrift Für Naturforschung A, 64(1–2), 96–102. https://doi.org/10.1515/zna-2009-1-214 51. Mandelbrot, B. B. (1985). Self-Affine Fractals and Fractal Dimension. Physica Scripta, 32(4), 257–260. https://doi.org/10.1088/0031- 8949/32/4/001 52. Marwan, N., Trauth, M. H., Vuille, M., & Kurths, J. (2003). Comparing modern and Pleistocene ENSO-like influences in NW Argentina using nonlinear time series analysis methods. Climate Dynamics, 21(3–4), 317–326. https://doi.org/10.1007/s00382-003-0335-3 53. Marwan, N., Wessel, N., Meyerfeldt, U., Schirdewan, A., & Kurths, J. (2002). Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Physical Review E, 66(2), Article 026702. https://doi.org/10.1103/physreve.66.026702 54. Moore, J. M., Corrêa, D. C., & Small, M. (2018). Is Bach’s brain a Markov chain? Recurrence quantification to assess Markov order for short, symbolic, musical compositions. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(8), Article 085715. https://doi.org/10.1063/1.5024814 55. Nair, V., & Sujith, R. I. (2013). Identifying homoclinic orbits in the dynamics of intermittent signals through recurrence quantification. Chaos: An Interdisciplinary Journal of Nonlinear Science, 23(3), Article 033136. https://doi.org/10.1063/1.4821475 56. Nichols, J., Trickey, S., & Seaver, M. (2006). Damage detection using multivariate recurrence quantification analysis. Mechanical Systems and Signal Processing, 20(2), 421–437. https://doi.org/10.1016/ j.ymssp.2004.08.007 57. Palmieri, F., & Fiore, U. (2009). A nonlinear, recurrence-based approach to traffic classification. Computer Networks, 53(6), 761–773. https://doi.org/10.1016/j.comnet.2008.12.015 58. Pham, T. D. (2016). Fuzzy recurrence plots. Europhysics Letters, 116(5), Article 50008. https://doi.org/10.1209/0295-5075/116/50008 59. Pham, T. D. (2019). Fuzzy weighted recurrence networks of time series. Physica A: Statistical Mechanics and its Applications, 513, 409–417. https://doi.org/10.1016/j.physa.2018.09.035 60. Pham, T. D. (2020). Fuzzy cross and fuzzy joint recurrence plots. Physica A: Statistical Mechanics and its Applications, 540, Article 123026. https://doi.org/10.1016/j.physa.2019.123026 61. Pham, T. D. (2020). Fuzzy recurrence entropy. Europhysics Letters, 130(4), Article 40004. https://doi.org/10.1209/0295-5075/130/40004 62. Pham, T. D. (2020). Fuzzy recurrence plots. In Fuzzy Recurrence Plots and Networks with Applications in Biomedicine (pp. 29-55). Springer. https://doi.org/10.1007/978-3-030-37530-0_4 63. Pham, T. D., Wardell, K., Eklund, A., & Salerud, G. (2019). Classification of short time series in early Parkinsons disease with deep learning of fuzzy recurrence plots. IEEE/CAA Journal of Automatica Sinica, 6(6), 1306–1317. https://doi.org/10.1109/jas.2019.1911774 64. Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America, 88(6), 2297–2301. https://doi.org/10.1073/pnas.88.6.2297 65. Pincus, S. M., & Goldberger, A. L. (1994). Physiological time-series analysis: what does regularity quantify? American Journal of Physiology- Heart and Circulatory Physiology, 266(4), H1643–H1656. https://doi.org/10.1152/ajpheart.1994.266.4.h1643 66. Pincus, S. M., & Huang, W. M. (1992). Approximate entropy: Statistical properties and applications. Communications in Statistics – Theory and Methods, 21(11), 3061–3077. https://doi.org/10.1080/03610929208830963 67. Qian, Y., Yan, R., & Hu, S. (2014). Bearing Degradation Evaluation Using Recurrence Quantification Analysis and Kalman Filter. IEEE Transactions on Instrumentation and Measurement, 63(11), 2599–2610. https://doi.org/10.1109/tim.2014.2313034 68. Rand, R., & Holmes, P. (1980). Bifurcation of periodic motions in two weakly coupled van der Pol oscillators. International Journal of Non- Linear Mechanics, 15(4–5), 387–399. https://doi.org/10.1016/0020- 7462(80)90024-4 69. Reinertsen, E., Osipov, M., Liu, C., Kane, J. M., Petrides, G., & Clifford, G. D. (2017). Continuous assessment of schizophrenia using heart rate and accelerometer data. Physiological Measurement, 38(7), 1456–1471. https://doi.org/10.1088/1361-6579/aa724d 70. Richardson, D. C., & Dale, R. (2005). Looking to Understand: The Coupling Between Speakers’ and Listeners’ Eye Movements and Its Relationship to Discourse Comprehension. Cognitive Science, 29(6), 1045– 1060. https://doi.org/10.1207/s15516709cog0000_29 71. Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039–H2049. https://doi.org/10.1152/ajpheart.2000.278.6.h2039 72. Roncagliolo Barrera, P., Rodríguez Gómez, F., & García Ochoa, E. (2019). Assessing of New Coatings for Iron Artifacts Conservation by Recurrence Plots Analysis. Coatings, 9(1), Article 12. https://doi.org/10.3390/coatings9010012 73. Rostaghi, M., & Azami, H. (2016). Dispersion Entropy: A Measure for Time-Series Analysis. IEEE Signal Processing Letters, 23(5), 610–614. https://doi.org/10.1109/lsp.2016.2542881 74. Ruelle, D. (1981). Small random perturbations of dynamical systems and the definition of attractors. Communications in Mathematical Physics, 82(1), 137–151. https://doi.org/10.1007/bf01206949 75. Sanchez-Roger, M., Oliver-Alfonso, M. D., & Sanchís-Pedregosa, C. (2019). Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises. Mathematics, 7(11), Article 1091. https://doi.org/10.3390/math7111091 76. Serrà, J., Serra, X., & Andrzejak, R. G. (2009). Cross recurrence quantification for cover song identification. New Journal of Physics, 11, Article 093017. https://doi.org/10.1088/1367-2630/11/9/093017 77. Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/ j.1538-7305.1948.tb01338.x 78. Shao, W., & Wang, J. (2020). Does the “ice-breaking” of South and North Korea affect the South Korean financial market? Chaos, Solitons & Fractals, 132, Article 109564. https://doi.org/10.1016/ j.chaos.2019.109564 79. Shaw, R. (1981). Strange Attractors, Chaotic Behavior, and Information Flow. Zeitschrift Für Naturforschung A, 36(1), 80–112. https://doi.org/10.1515/zna-1981-0115 80. Shi, B., Wang, L., Yan, C., Chen, D., Liu, M., & Li, P. (2019). Nonlinear heart rate variability biomarkers for gastric cancer severity: A pilot study. Scientific Reports, 9(1), Article 13833. https://doi.org/10.1038/s41598-019-50358-y 81. Silva, D. F., De Souza, V. M., & Batista, G. E. (2013). Time series classification using compression distance of recurrence plots. In Proceedings of 2013 IEEE 13th International Conference on Data Mining (pp. 687-696). IEEE. https://doi.org/10.1109/ICDM.2013.128 82. Soloviev, V., & Belinskiy, A. (2018). Complex systems theory and crashes of cryptocurrency market. In V. Ermolayev, M. Suárez-Figueroa, V. Yakovyna, H. Mayr, M. Nikitchenko, & A. Spivakovsky (Eds.), Communications in Computer and Information Science: Vol. 1007. Information and Communication Technologies in Education, Research, and Industrial Applications (pp. 276-297). Springer. https://doi.org/10.1007/978- 3-030-13929-2_14 83. Soloviev, V., & Belinskij, A. (2018). Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors. CEUR Workshop Proceedings, 2104, 116-127. http://ceur- ws.org/Vol-2104/paper_175.pdf 84. Soloviev, V., Bielinskyi, A., & Kharadzjan, N. (2020). Coverage of the coronavirus pandemic through entropy measures. CEUR Workshop Proceedings, 2832, 24-42. http://ceur-ws.org/Vol-2832/paper02.pdf 85. Soloviev, V., Bielinskyi, A., Serdyuk, O., Solovieva, V., & Semerikov, S. (2020). Lyapunov exponents as indicators of the stock market crashes. CEUR Workshop Proceedings, 2732, 455-470. http://ceur- ws.org/Vol-2732/20200455.pdf 86. Soloviev, V., Bielinskyi, A., & Solovieva, V. (2019). Entropy analysis of crisis phenomena for DJIA index. CEUR Workshop Proceedings, 2393, 434-449. http://ceur-ws.org/Vol-2393/paper_375.pdf 87. Soloviev, V., Serdiuk, O., Semerikov, S., & Kohut-Ferens, O. (2019). Recurrence entropy and financial crashes. Advances in Economics, Business and Management Research, 99, 385-388. https://dx.doi.org/10.2991/mdsmes-19.2019.73 88. Stangalini, M., Ermolli, I., Consolini, G., & Giorgi, F. (2017). Recurrence quantification analysis of two solar cycle indices. Journal of Space Weather and Space Climate, 7, Article A5. https://doi.org/10.1051/swsc/2017004 89. Stender, M., Oberst, S., Tiedemann, M., & Hoffmann, N. (2019). Complex machine dynamics: systematic recurrence quantification analysis of disk brake vibration data. Nonlinear Dynamics, 97(4), 2483–2497. https://doi.org/10.1007/s11071-019-05143-x 90. Strozzi, F., Zaldı́ Var, J. M., & Zbilut, J. P. (2002). Application of nonlinear time series analysis techniques to high-frequency currency exchange data. Physica A: Statistical Mechanics and its Applications, 312(3– 4), 520–538. https://doi.org/10.1016/s0378-4371(02)00846-4 91. Takens, F. (1981). Detecting strange attractors in turbulence. In D. Rand, & L. Young (Eds.), Lecture Notes in Mathematics: Vol. 898. Dynamical systems and turbulence, Warwick 1980 (pp. 366-381). Springer- Verlag. https://doi.org/10.1007/BFb0091924 92. Voss, A., Schroeder, R., Vallverdu, M., Cygankiewicz, I., Vazquez, R., de Luna, A. B., & Caminal, P. (2008). Linear and nonlinear heart rate variability risk stratification in heart failure patients. In Proceedings of the 2008 Computers in Cardiology (pp. 557-560). IEEE. https://doi.org/10.1109/ CIC.2008.4749102 93. Wang, G., & Wang, J. (2017). New approach of financial volatility duration dynamics by stochastic finite-range interacting voter system. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27(1), Article 013117. https://doi.org/10.1063/1.4974216 94. Wang, Y., Zheng, S., Zhang, W., Wang, G., & Wang, J. (2018). Fuzzy entropy complexity and multifractal behavior of statistical physics financial dynamics. Physica A: Statistical Mechanics and its Applications, 506, 486–498. https://doi.org/10.1016/j.physa.2018.04.086 95. Webber, C. L., & Zbilut, J. P. (1994). Dynamical assessment of physiological systems and states using recurrence plot strategies. Journal of Applied Physiology, 76(2), 965–973. https://doi.org/10.1152/jappl.1994.76.2.965 96. Xie, H. B., He, W. X., & Liu, H. (2008). Measuring time series regularity using nonlinear similarity-based sample entropy. Physics Letters A, 372(48), 7140–7146. https://doi.org/10.1016/j.physleta.2008.10.049 97. Xie, H. B., Sivakumar, B., Boonstra, T. W., & Mengersen, K. (2018). Fuzzy Entropy and Its Application for Enhanced Subspace Filtering. IEEE Transactions on Fuzzy Systems, 26(4), 1970–1982. https://doi.org/10.1109/tfuzz.2017.2756829 98. Yang, Y. G., Pan, Q. X., Sun, S. J., & Xu, P. (2015). Novel Image Encryption based on Quantum Walks. Scientific Reports, 5(1), Article 7784. https://doi.org/10.1038/srep07784 99. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338– 353. https://doi.org/10.1016/S0019-9958(65)90241-X 100. Zaitouny, A., Walker, D. M., & Small, M. (2019). Quadrant scan for multi-scale transition detection. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(10), Article 103117. https://doi.org/10.1063/1.5109925 101. Zbilut, J. P., & Marwan, N. (2008). The Wiener–Khinchin theorem and recurrence quantification. Physics Letters A, 372(44), 6622–6626. https://doi.org/10.1016/j.physleta.2008.09.027 102. Zhang, Z., Xiang, Z., Chen, Y., & Xu, J. (2020). Fuzzy permutation entropy derived from a novel distance between segments of time series. AIMS Mathematics, 5(6), 6244–6260. https://doi.org/10.3934/math.2020402 103. Zhao, H., Deng, W., Yao, R., Sun, M., Luo, Y., & Dong, C. (2017). Study on a novel fault diagnosis method based on integrating EMD, fuzzy entropy, improved PSO and SVM. Journal of Vibroengineering, 19(4), 2562–2577. https://doi.org/10.21595/jve.2017.18052 104. Zhao, X., & Zhang, P. (2020). Multiscale horizontal visibility entropy: Measuring the temporal complexity of financial time series. Physica A: Statistical Mechanics and its Applications, 537, Article 122674. https://doi.org/10.1016/j.physa.2019.122674 105. Zhao, Z. Q., Li, S. C., Gao, J. B., & Wang, Y. L. (2011). Identifying Spatial Patterns and Dynamics of Climate Change Using Recurrence Quantification Analysis: A Case Study of Qinghai–Tibet Plateau. International Journal of Bifurcation and Chaos, 21(04), 1127-1139. https://doi.org/10.1142/s0218127411028933 106. Zhou, C., & Zhang, W. (2015). Recurrence Plot Based Damage Detection Method by Integrating T 2 Control Chart. Entropy, 17(5), 2624- 2641. https://doi.org/10.3390/e17052624 107. Zhou, Q., & Shang, P. (2020). Weighted multiscale cumulative residual Rényi permutation entropy of financial time series. Physica A: Statistical Mechanics and its Applications, 540, Article 123089. https://doi.org/10.1016/j.physa.2019.123089 108. Zhou, R., Wang, X., Wan, J., & Xiong, N. (2021). EDM-Fuzzy: An Euclidean Distance Based Multiscale Fuzzy Entropy Technology for Diagnosing Faults of Industrial Systems. IEEE Transactions on Industrial Informatics, 17(6), 4046–4054. https://doi.org/10.1109/tii.2020.3009139 109. Zolotova, N. V., & Ponyavin, D. I. (2006). Phase asynchrony of the north-south sunspot activity. Astronomy & Astrophysics, 449(1), L1–L4. https://doi.org/10.1051/0004-6361:200600013
URI (Уніфікований ідентифікатор ресурсу): http://doi.org/10.33111/nfmte.2021.003
http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/7003
ISSN: 2415-3516
Розташовується у зібраннях:Кафедра інформатики та прикладної математики

Файли цього матеріалу:
Файл Опис РозмірФормат 
Identifying stock market crashes by fuzzy measures of complexity-1.pdf3.46 MBAdobe PDFПереглянути/Відкрити
Показати повний опис матеріалу Перегляд статистики


Усі матеріали в архіві електронних ресурсів захищені авторським правом, всі права збережені.