EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 107 (2021) SHS Web Conf., 107 (2021) 03001 References
Open Access
Issue
SHS Web Conf.
Volume 107, 2021
9th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2021)
Article Number 03001
Number of page(s) 59
Section Econophysics
DOI https://doi.org/10.1051/shsconf/202110703001
Published online 24 May 2021
  1. R. Albert, A.L. Barabási, Rev. Mod. Phys. 74, 47 (2002) [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  2. C. Reuven, H. Shlomo, Complex Networks: Structure, Robustness and Function (Cambridge University Press, 2010), ISBN 9780521841566 [CrossRef] [Google Scholar]
  3. M.E.J. Newman, SIAM Review 45, 167–256 (2003) [CrossRef] [MathSciNet] [Google Scholar]
  4. M. Newman, A.L. Barabasi, D.J. Watts, eds., The Structure and Dynamics of Networks (Princeton University Press, Princeton, NJ, USA, 2006) [Google Scholar]
  5. G. Nicolis, I. Prigogine, Exploring complexity: an introduction (W.H. Freeman, 1989), ISBN 9780716718598 [Google Scholar]
  6. A. Rai, A. Mahata, M. Nurujjaman, O. Prakash, Statistical properties of the aftershocks of stock market crashes: evidence based on the 1987 crash, 2008 financial crisis and COVID-19 pandemic (2020), arXiv:2012.03012 [q-fin.ST] [Google Scholar]
  7. E. Mnif, A. Jarboui, K. Mouakhar, Finance Research Letters 36, 101647 (2020) [CrossRef] [Google Scholar]
  8. A. Ammy-Driss, M. Garcin, Eflciency of the financial markets during the COVID-19 crisis: timevarying parameters of fractional stable dynamics (2020), arXiv:2007.10727 [q-fin.ST] [Google Scholar]
  9. R. Cerqueti, V. Ficcadenti, Anxiety for the pandemic and trust in financial markets (2020), arXiv:2008.01649 [q-fin.ST] [Google Scholar]
  10. M. Costola, M. Iacopini, C.R.M.A. Santagiustina, Public Concern and the Financial Markets during the COVID-19 outbreak (2020), arXiv:2005.06796 [q-fin.ST] [Google Scholar]
  11. M. Feldkircher, F. Huber, M. Pfarrhofer, Measuring the Effectiveness of US Monetary Policy during the COVID-19 Recession (2020), arXiv:2007.15419 [econ.EM] [Google Scholar]
  12. M. Garcin, J. Klein, S. Laaribi, Estimation of timevarying kernel densities and chronology of the impact of COVID-19 on financial markets (2020), arXiv:2007.09043 [q-fin.ST] [Google Scholar]
  13. M. Pagano, C. Wagner, J. Zechner, Disaster resilience and asset prices (2020), arXiv:2005.08929 [q-fin.GN] [Google Scholar]
  14. A.A. Toda, Susceptible-Infected-Recovered (SIR) Dynamics of COVID-19 and Economic Impact (2020), arXiv:2003.11221 [q-bio.PE] [Google Scholar]
  15. S. Droz˙dz˙, J. Kwapien´, P. Os´wie¸, T. Stanisz, M. Wa¸torek, Entropy 22 (2020) [Google Scholar]
  16. R.K.K. Pang, O. Granados, H. Chhajer, E.F. Legara, An analysis of network filtering methods to sovereign bond yields during COVID-19 (2021), arXiv:2009.13390 [q-fin.ST] [Google Scholar]
  17. S. Semerikov, S. Chukharev, S. Sakhno, A. Striuk, V. Osadchyi, V. Solovieva, T. Vakaliuk, P. Nechypurenko, O. Bondarenko, H. Danylchuk, E3S Web of Conferences 166 (2020) [Google Scholar]
  18. H. Danylchuk, L. Kibalnyk, O. Kovtun, A. Kiv, O. Pursky, G. Berezhna, CEUR Workshop Proceedings 2713, 352 (2020) [Google Scholar]
  19. A. Kaminskyi, M. Nehrey, N. Rizun, CEUR Workshop Proceedings 2713, 204 (2020) [Google Scholar]
  20. N. Maksyshko, O. Vasylieva, I. Kozin, V. Perepelitsa, CEUR Workshop Proceedings 2713, 219 (2020) [Google Scholar]
  21. S. Semerikov, H. Kucherova, V. Los, D. Ocheretin, CEUR Workshop Proceedings 2845, 22 (2021) [Google Scholar]
  22. G. Malinetsky, Modeling and Analysis of Information Systems 19, 5 (2015) [CrossRef] [Google Scholar]
  23. V. Soloviev, N. Moiseienko, O. Tarasova, CEUR Workshop Proceedings 2393, 905 (2019) [Google Scholar]
  24. S. Somin, Y. Altshuler, G. Gordon, A. Pentland, E. Shmueli, Scientific Reports 10, 4587 (2020) [CrossRef] [Google Scholar]
  25. P. Grau, C. Jaureguizar, D. Jaureguizar, Esic Market Economics and Business Journal 49, 569 (2018) [CrossRef] [Google Scholar]
  26. J. Liang, L. Li, D. Zeng, PLOS ONE 13, 1 (2018) [CrossRef] [Google Scholar]
  27. S. Thurner, P. Klimek, R. Hanel, Introduction to the theory of complex systems (Oxford University Press, Oxford, 2018) [Google Scholar]
  28. S. Droz˙dz˙, L. Minati, P. Os´wie¸cimka, M. Stanuszek, M. Wa¸torek, Chaos: An Interdisciplinary Journal of Nonlinear Science 30, 023122 (2020) [CrossRef] [Google Scholar]
  29. M. Wa¸torek, S. Droz˙dz˙, J. Kwapien´, L. Minati, P. Os´wie¸cimka, M. Stanuszek, Physics Reports 901, 1 (2021), multiscale characteristics of the emerging global cryptocurrency market [Google Scholar]
  30. A.O. Bielinskyi, I. Khvostina, A. Mamanazarov, A. Matviychuk, S. Semerikov, O. Serdyuk, V. Solovieva, V.N. Soloviev, IOP Conference Series: Earth and Environmental Science 628, 012019 (2021) [CrossRef] [Google Scholar]
  31. M. Ausloos, D. Grech, T.D. Matteo, R. Kutner, C. Schinckus, H.E. Stanley, Physica A: Statistical Mechanics and its Applications 531, 136 (2020) [Google Scholar]
  32. W.B. Arthur, Nature Reviews Physics 3, 136 (2021) [CrossRef] [Google Scholar]
  33. R. Kutner, M. Ausloos, D. Grech, T. Di Matteo, C. Schinckus, H. Eugene Stanley, Physica A: Statistical Mechanics and its Applications 516, 240 (2019) [CrossRef] [Google Scholar]
  34. R. Mantegna, H. Stanley, An Introduction to Econophysics: Correlations and Complexity in Finance, Vol. 53 (2000) [Google Scholar]
  35. Z.Q. Jiang, W.J. Xie, W.X. Zhou, D. Sornette, Reports on Progress in Physics 82, 125901 (2019) [CrossRef] [Google Scholar]
  36. J. Kwapien´, S. Droz˙dz˙, Physics Reports 515, 115 (2012), physical approach to complex systems [Google Scholar]
  37. B.E. Baaquie, Quantum Finance: Path Integrals and Hamiltonians for Options and Interest Rates (Cambridge University Press, 2004) [CrossRef] [Google Scholar]
  38. M. Schaden, Physica A: Statistical Mechanics and its Applications 316, 511–538 (2002) [CrossRef] [Google Scholar]
  39. V.P. Maslov, V.E. Nazaikinskii, Mathematics underlying the 2008 financial crisis, and a possible remedy (2009), arXiv:0811.4678 [q-fin.GN] [Google Scholar]
  40. C. Schinckus, A Methodological Call for a Quantum Econophysics, in Selected Papers of the 7th International Conference on Quantum Interaction Volume 8369 (Springer-Verlag, Berlin, Heidelberg, 2013), QI 2013, p. 308–316 [Google Scholar]
  41. V. Saptsin, V. Soloviev, Relativistic quantum econophysics new paradigms in complex systems modelling (2009), arXiv:0907.1142 [physics.soc-ph] [Google Scholar]
  42. V. Soloviev, V. Saptsin, Heisenberg uncertainty principle and economic analogues of basic physical quantities (2011), arXiv:1111.5289 [physics.gen-ph] [Google Scholar]
  43. D. Sornette, Why Stock Markets Crash: Critical Events in Complex Financial Systems (Princeton University Press, 2003) [Google Scholar]
  44. Y. Peng, P. Albuquerque, J. Camboim de Sá, A.J. Padula, M. Montenegro, Expert Systems with Applications 97 (2017) [Google Scholar]
  45. Q. Zhao, A deep learning framework for predicting digital asset price movement from trade-by-trade data (2020), arXiv:2010.07404 [q-fin.ST] [Google Scholar]
  46. M. Amjad, D. Shah, Trading Bitcoin and Online Time Series Prediction, in Proceedings of the Time Series Workshop at NIPS 2016, edited by O. Anava, A. Khaleghi, M. Cuturi, V. Kuznetsov, A. Rakhlin (PMLR, Barcelona, Spain, 2017), Vol. 55 of Proceedings of Machine Learning Research [Google Scholar]
  47. T. Chen, C. Guestrin, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016) [Google Scholar]
  48. V. Derbentsev, A. Matviychuk, V. Soloviev, Forecasting of Cryptocurrency Prices Using Machine Learning (Springer, 2020), pp. 211–231, 1st edn., ISBN 978-981-15-4498-9 [Google Scholar]
  49. A.H. Dyhrberg, Finance Research Letters 16, 85 (2016) [CrossRef] [Google Scholar]
  50. M. Ortu, N. Uras, C. Conversano, G. Destefanis, S. Bartolucci, On technical trading and social media indicators in cryptocurrencies’ price classification through deep learning (2021), arXiv:2102.08189 [q-fin.ST] [Google Scholar]
  51. N. Uras, L. Marchesi, M. Marchesi, R. Tonelli, Forecasting bitcoin closing price series using linear regression and neural networks models (2020), arXiv:2001.01127 [q-fin.ST] [Google Scholar]
  52. A. Hachicha, F. Hachicha, Review of Quantitative Finance and Accounting 56, 647 (2021) [CrossRef] [Google Scholar]
  53. J. Kaminski, Nowcasting the bitcoin market with twitter signals (2016), arXiv:1406.7577 [cs.SI] [Google Scholar]
  54. M.A. Kennis, Multi-channel discourse as an indicator for bitcoin price and volume movements (2018), arXiv:1811.03146 [q-fin.ST] [Google Scholar]
  55. O. Kodama, L. Pichl, T. Kaizoji, CBU International Conference Proceedings 5, 384 (2017) [CrossRef] [Google Scholar]
  56. D. Shah, K. Zhang, Bayesian regression and bitcoin (2014), arXiv:1410.1231 [cs.AI] [Google Scholar]
  57. V. Soloviev, S. V., in Modern problems of forecasting socio-economic processes: concepts, models, applied aspects (Tkachuk O. V., 2012), pp. 174–188 [Google Scholar]
  58. M. Tarnopolski, Modeling the price of bitcoin with geometric fractional brownian motion: a monte carlo approach (2017), arXiv:1707.03746 [q-fin.CP] [Google Scholar]
  59. N. Courtois, M. Grajek, R. Naik, Optimizing SHA256 in Bitcoin Mining (2014), Vol. 448 of Communications in Computer and Information Science, pp. 131–144, ISBN 978-3-662-44892-2 [CrossRef] [Google Scholar]
  60. L. Kristoufek, Frontiers in Physics 8 (2020) [CrossRef] [Google Scholar]
  61. D. Broomhead, G.P. King, Physica D: Nonlinear Phenomena 20, 217 (1986) [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  62. V.I. Ponomarenko, M.D. Prokhorov, Phys. Rev. E 66, 026215 (2002) [CrossRef] [Google Scholar]
  63. M. Rajkovic´, Physica A: Statistical Mechanics and its Applications 287, 383 (2000) [CrossRef] [Google Scholar]
  64. G.M. Caporale, L.A. Gil-Alana, A. Plastun, CESifo Working Paper Series 6811, CESifo (2017), https://ideas.repec.org/p/ces/ceswps/_6811.html [Google Scholar]
  65. A.F. Bariviera, M.J. Basgall, W. Hasperué, M. Naiouf, Physica A: Statistical Mechanics and its Applications 484, 82–90 (2017) [CrossRef] [Google Scholar]
  66. A.F. Bariviera, Economics Letters 161, 1–4 (2017) [CrossRef] [Google Scholar]
  67. J. Wang, G. Meric, Z. Liu, I. Meric, Journal of Banking & Finance 33, 1563 (2009) [CrossRef] [Google Scholar]
  68. S. Lleo, W.T. Ziemba, Financial Markets, Institutions & Instruments 26, 61 (2017) [CrossRef] [Google Scholar]
  69. H. Hong, J. Stein, Review of Financial Studies 16, 487 (2003) [CrossRef] [Google Scholar]
  70. M. Shu, W. Zhu, Physica A: Statistical Mechanics and its Applications 548, 124477 (2020) [CrossRef] [Google Scholar]
  71. T. Klein, H. Pham Thu, T. Walther, International Review of Financial Analysis 59, 105 (2018) [CrossRef] [Google Scholar]
  72. K. Gkillas, F. Longin, SSRN Electronic Journal pp. 1–85 (2019) [Google Scholar]
  73. Y. Liu, A. Tsyvinski, X. Wu, NBER Working Papers 25882, National Bureau of Economic Research, Inc (2019) [Google Scholar]
  74. L. Kristoufek, PLOS ONE 10, 1 (2015) [CrossRef] [Google Scholar]
  75. X. Li, C. Wang, Decision Support Systems 95, 49 (2017) [CrossRef] [Google Scholar]
  76. A. Bielinskyi, V. Soloviev, S. Semerikov, V. Solovieva, CEUR Workshop Proceedings 2422, 420 (2019) [Google Scholar]
  77. A. Bielinskyi, S. Semerikov, V. Solovieva, V. Soloviev, SHS Web Conf. 65, 06006 (2019) [CrossRef] [EDP Sciences] [Google Scholar]
  78. V. Derbentsev, S. Semerikov, O. Serdyuk, V. Solovieva, V. Soloviev, E3S Web Conf. 166, 13031 (2020) [CrossRef] [EDP Sciences] [Google Scholar]
  79. V. Soloviev, A. Belinskiy, Complex Systems Theory and Crashes of Cryptocurrency Market (Springer, Cham, 2019), Vol. 1007 of ICTERI 2018, pp. 276–297, ISBN 978-3-030-13928-5 [Google Scholar]
  80. V. Soloviev, A. Belinskiy, CEUR Workshop Proceedings 2104, 116 (2018) [Google Scholar]
  81. V. Soloviev, A. Bielinskyi, O. Serdyuk, V. Solovieva, S. Semerikov, CEUR Workshop Proceedings 2732, 455 (2020) [Google Scholar]
  82. V. Soloviev, S. Yevtushenko, V. Batareyev, CEUR Workshop Proceedings 2393, 434 (2019) [Google Scholar]
  83. V. Soloviev, V. Solovieva, A. Tuliakova, M. Ivanova, Construction of crisis precursors in multiplex networks, in Proceedings of the 2019 7th International Conference on Modeling, Development and Strategic Management of Economic System (MDSMES 2019) (Atlantis Press, 2019/10), pp. 361–366, ISBN 978-94-6252-800-0, ISSN 2352-5428, https://doi.org/10.2991/mdsmes-19.2019.68 [Google Scholar]
  84. V. Soloviev, O. Serdiuk, S. Semerikov, O. KohutFerens, Recurrence entropy and financial crashes, in Proceedings of the 2019 7th International Conference on Modeling, Development and Strategic Management of Economic System (MDSMES 2019) (Atlantis Press, 2019/10), pp. 385–388, ISBN 97894-6252-800-0, ISSN 2352-5428, https://doi.org/10.2991/mdsmes-19.2019.73 [Google Scholar]
  85. V. Soloviev, A. Bielinskyi, N. Kharadzjan, CEUR Workshop Proceedings 2832, 24 (2020) [Google Scholar]
  86. M.S. Kanwal, J.A. Grochow, N. Ay, Entropy 19 (2017) [Google Scholar]
  87. D. Bonchev, Information Theoretic Complexity Measures (Springer, 2009), pp. 4820–4838 [Google Scholar]
  88. L. Lovász, The Emergence of Complexity in Mathematics, Physics, Chemistry and Biology, Pontifical Academy of Sciences p. 12 (1996) [Google Scholar]
  89. L.T. Lui, G. Terrazas, H. Zenil, C. Alexander, N. Krasnogor, Artificial Life 21, 205 (2015) [CrossRef] [Google Scholar]
  90. C.E. Shannon, Bell System Technical Journal 27, 379 (1948) [CrossRef] [MathSciNet] [Google Scholar]
  91. G. Sommazzi, Ph.D. thesis (2016) [Google Scholar]
  92. J.L. Blanc, L. Pezard, A. Lesne, Phys. Rev. E 84, 036214 (2011) [CrossRef] [Google Scholar]
  93. S. Zozor, P. Ravier, O. Buttelli, Physica A: Statistical Mechanics and its Applications 345, 285 (2005) [CrossRef] [Google Scholar]
  94. E. Estevez-Rams, R. Lora Serrano, B. Aragón Fernández, I. Brito Reyes, Chaos: An Interdisciplinary Journal of Nonlinear Science 23, 023118 (2013) [CrossRef] [Google Scholar]
  95. S.D. Silva, R. Matsushita, R. Giglio, Economics Bulletin 7, 1 (2008) [Google Scholar]
  96. R. Giglio, R. Matsushita, A. Figueiredo, I. Gleria, S.D. Silva, EPL (Europhysics Letters) 84, 48005 (2008) [Google Scholar]
  97. S. Da Silva, MPRA Paper 64497, University Library of Munich, Germany (2015) [Google Scholar]
  98. S. Da Silva, C. Taufemback, R. Giglio, Economics Bulletin 31, 1631 (2011) [Google Scholar]
  99. R. Giglio, S. Da Silva, MPRA Paper 22720, University Library of Munich, Germany (2009) [Google Scholar]
  100. A. Lempel, J. Ziv, IEEE Transactions on Information Theory 22, 75 (1976) [Google Scholar]
  101. O. Brandouy, J.P. Delahaye, L. Ma, H. Zenil, Research in International Business and Finance 30, 336 (2014) [Google Scholar]
  102. P. Fiedor, Risks 3, 219 (2015) [Google Scholar]
  103. J. Gao, Y. Hou, F. Fan, F. Liu, Entropy 22 (2020) [Google Scholar]
  104. H. Cao, Y. Li, Chaos: An Interdisciplinary Journal of Nonlinear Science 24, 013134 (2014) [Google Scholar]
  105. V. Soloviev, S. Semerikov, V. Solovieva, LempelZiv Complexity and Crises of Cryptocurrency Market, in Proceedings of the III International Scientific Congress Society of Ambient Intelligence 2020 (ISC-SAI 2020) (Atlantis Press, 2020), pp. 299–306, ISBN 978-94-6252-933-5, ISSN 2352-5428, https://doi.org/10.2991/aebmr.k.200318.037 [Google Scholar]
  106. A.N. Kolmogorov, International Journal of Computer Mathematics 2, 157 (1968) [Google Scholar]
  107. M. Costa, C.K. Peng, A. Goldberger, Cardiovascular engineering (Dordrecht, Netherlands) 8, 88 (2008) [Google Scholar]
  108. R. Clausius, T. Hirst, The Mechanical Theory of Heat: With Its Applications to the Steam-Engine and to the Physical Properties of Bodies (Creative Media Partners, LLC, 2017) [Google Scholar]
  109. L. Boltzmann, Weitere Studien über das Wärmegleichgewicht unter Gasmolekülen (Vieweg+Teubner Verlag, 1970), Vol. 67, pp. 115–225 [Google Scholar]
  110. J.W. Gibbs, Elementary principles in statistical mechanics developed with especial reference to the rational foundation of thermodynamics (C. Scribner, New York, 1902) [Google Scholar]
  111. M. Vosvrda, J. Barunik, L. Vacha, M. Vošvrda, Czech Economic Review 4, 281 (2010) [Google Scholar]
  112. J.S. Richman, J.R. Moorman, American Journal of Physiology-Heart and Circulatory Physiology 278, H2039 (2000) [CrossRef] [PubMed] [Google Scholar]
  113. R. Sole, S. Valverde, Information Theory of Complex Networks: On Evolution and Architectural Constraints (Springer, 2004), Vol. 207, pp. 189–207, ISBN 978-3-540-22354-2 [Google Scholar]
  114. V. Soloviev, O. Serdiuk, in Information technologies and modeling in economics: on the way to interdisciplinarity (Gate-Ukraine, 2013), pp. 115–129 [Google Scholar]
  115. C. Tsallis, Introduction to nonextensive statistical mechanics: Approaching a complex world (Springer, 2009), ISBN 978-0-387-85358-1 [Google Scholar]
  116. A. Delgado-Bonal, A. Marshak, Entropy 21 (2019) [Google Scholar]
  117. S. Pincus, Proceedings of the National Academy of Sciences of the United States of America 88, 2297—2301 (1991) [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  118. S.M. Pincus, A.L. Goldberger, American Journal of Physiology-Heart and Circulatory Physiology 266, H1643 (1994) [CrossRef] [Google Scholar]
  119. K. Yun, H.K. Park, D.H. Kwon, Y.T. Kim, S.N. Cho, H.J. Cho, B.S. Peterson, J. Jeong, Psychiatry Research: Neuroimaging 201, 226 (2012) [CrossRef] [Google Scholar]
  120. S.N. Bhaduri, Journal of Emerging Market Finance 13, 43 (2014) [CrossRef] [Google Scholar]
  121. C. Eom, G. Oh, W.S. Jung, Physica A: Statistical Mechanics and its Applications 387, 5511 (2008) [CrossRef] [Google Scholar]
  122. S. Lahmiri, S. Bekiros, Chaos, Solitons & Fractals 138, 109936 (2020) [CrossRef] [Google Scholar]
  123. I. Mahmoud, K. Naoui, H. Jemmali, International Journal of Economics and Financial Issues 3, 683 (2013) [Google Scholar]
  124. S. Pincus, R.E. Kalman, Proceedings of the National Academy of Sciences 101, 13709 (2004) [CrossRef] [MathSciNet] [Google Scholar]
  125. W.Q. Duan, H.E. Stanley, Phys. Rev. E 81, 066116 (2010) [CrossRef] [Google Scholar]
  126. A. Delgado-Bonal, Sci. Rep. 9, 2045 (2019) [CrossRef] [Google Scholar]
  127. C. Bandt, B. Pompe, Phys. Rev. Lett. 88, 174102 (2002) [CrossRef] [PubMed] [Google Scholar]
  128. J. Amigó, Permutation Complexity in Dynamical Systems (Springer-Verlag Berlin Heidelberg, 2010) [CrossRef] [Google Scholar]
  129. M. Zanin, L. Zunino, O.A. Rosso, D. Papo, Entropy 14, 1553 (2012) [CrossRef] [Google Scholar]
  130. H. Kantz, T. Schreiber, Nonlinear Time Series Analysis, 2nd edn. (Cambridge University Press, 2003) [CrossRef] [Google Scholar]
  131. M. Henry, G. Judge, Econometrics 7 (2019) [Google Scholar]
  132. H. Sigaki, M. Perc, H. Ribeiro, Scientific Reports 9 (2019) [CrossRef] [Google Scholar]
  133. A. Sensoy, Finance Research Letters 28, 68 (2019) [CrossRef] [Google Scholar]
  134. A. Metin Karakas¸, Asian Journal of Business and Management 7, 10 (2019) [Google Scholar]
  135. D.T. Pele, M. Mazurencu-Marinescu-Pele, Entropy 21 (2019) [Google Scholar]
  136. F. Takens, in Dynamical Systems and Turbulence, Warwick 1980, edited by D. Rand, L.S. Young (Springer, Berlin, 1981), Vol. 898 of Lecture Notes in Mathematics, chap. 21, pp. 366–381 [NASA ADS] [CrossRef] [Google Scholar]
  137. J.P. Eckmann, D. Ruelle, Rev. Mod. Phys. 57, 617 (1985) [NASA ADS] [CrossRef] [Google Scholar]
  138. E. Ott, T. Sauer, J. Yorke, Coping with Chaos, Wiley Series in Nonlinear Science (Wiley, 1994) [Google Scholar]
  139. C. Webber, Jr., J. Zbilut, in Tutorials in Contemporary Nonlinear Methods for the Behavioral Sciences, edited by M.A. Riley, G.C.V. Orden (National Science Foundation (NSF), 2005), chap. 2 [Google Scholar]
  140. R. Gu, Physica A: Statistical Mechanics and its Applications 484, 215 (2017) [CrossRef] [Google Scholar]
  141. B.B. Mandelbrot, J.A. Wheeler, American Journal of Physics 51, 286 (1983) [CrossRef] [Google Scholar]
  142. H.E. Hurst, Transactions of the American Society of Civil Engineers 116, 770 (1951) [Google Scholar]
  143. H.E. Hurst, Nature 180, 494 (1957) [CrossRef] [Google Scholar]
  144. A.W. Lo, Working Paper 2984, National Bureau of Economic Research (1989) [Google Scholar]
  145. C.K. Peng, S.V. Buldyrev, S. Havlin, M. Simons, H.E. Stanley, A.L. Goldberger, Phys. Rev. E 49, 1685 (1994) [NASA ADS] [CrossRef] [Google Scholar]
  146. Z.Q. Jiang, W.J. Xie, W.X. Zhou, Physica A: Statistical Mechanics and its Applications 405, 235 (2014) [CrossRef] [Google Scholar]
  147. J.W. Kantelhardt, S.A. Zschiegner, E. KoscielnyBunde, S. Havlin, A. Bunde, H. Stanley, Physica A: Statistical Mechanics and its Applications 316, 87–114 (2002) [CrossRef] [Google Scholar]
  148. F. Aslam, W. Mohti, P. Ferreira, International Journal of Financial Studies 8 (2020) [Google Scholar]
  149. R. Hasan, S.M. Mohammad, Physica A: Statistical Mechanics and its Applications 419, 746 (2015) [CrossRef] [Google Scholar]
  150. S. Kumar, N. Deo, Physica A: Statistical Mechanics and its Applications 388, 1593 (2009) [CrossRef] [Google Scholar]
  151. J. Kwapien´, P. Os´wie¸cimka, S. Droz˙dz˙, Physica A: Statistical Mechanics and its Applications 350, 466 (2005) [CrossRef] [Google Scholar]
  152. S. Lahmiri, Physica A: Statistical Mechanics and its Applications 486, 183 (2017) [CrossRef] [Google Scholar]
  153. J.W. Lee, K. Eun Lee, P. Arne Rikvold, Physica A: Statistical Mechanics and its Applications 364, 355 (2006) [CrossRef] [Google Scholar]
  154. K. Matia, Y. Ashkenazy, H.E. Stanley, Europhysics Letters (EPL) 61, 422 (2003) [CrossRef] [Google Scholar]
  155. P. Suárez-García, D. Gómez-Ullate, Physica A: Statistical Mechanics and its Applications 394, 226 (2014) [CrossRef] [Google Scholar]
  156. L. Zunino, A. Figliola, B.M. Tabak, D.G. Pérez, M. Garavaglia, O.A. Rosso, Chaos, Solitons & Fractals 41, 2331 (2009) [CrossRef] [Google Scholar]
  157. F. Delbianco, F. Tohmé, T. Stosic, B. Stosic, Physica A: Statistical Mechanics and its Applications 457, 573 (2016) [CrossRef] [Google Scholar]
  158. R. Gu, H. Chen, Y. Wang, Physica A: Statistical Mechanics and its Applications 389, 2805 (2010) [CrossRef] [Google Scholar]
  159. Z. Li, X. Lu, Energy Procedia 5, 1920 (2011), 2010 International Conference on Energy, Environment and Development ICEED2010 [Google Scholar]
  160. P. Mali, A. Mukhopadhyay, Physica A: Statistical Mechanics and its Applications 413, 361–372 (2014) [CrossRef] [Google Scholar]
  161. S. Zheng, X. Lan, Physica A: Statistical Mechanics and its Applications 444, 547 (2016) [CrossRef] [Google Scholar]
  162. G. Lim, S. Kim, H. Lee, K. Kim, D.I. Lee, Physica A: Statistical Mechanics and its Applications 386, 259 (2007) [CrossRef] [Google Scholar]
  163. P. Caraiani, E. Haven, Physica A Statistical Mechanics and its Applications 419, 395 (2015) [CrossRef] [Google Scholar]
  164. P. Norouzzadeh, B. Rahmani, Physica A: Statistical Mechanics and its Applications 367, 328 (2006) [CrossRef] [Google Scholar]
  165. G. Oh, C. Eom, S. Havlin, W.S. Jung, F. Wang, H. Stanley, S. Kim, Eur. Phys. J. B 85, 214 (2012) [CrossRef] [EDP Sciences] [Google Scholar]
  166. J. Qin, X. Lu, Y. Zhou, L. Qu, Physica A: Statistical Mechanics and its Applications 421, 443 (2015) [CrossRef] [Google Scholar]
  167. D.H. Wang, X.W. Yu, Y.Y. Suo, Physica A: Statistical Mechanics and its Applications 391, 3503 (2012) [CrossRef] [Google Scholar]
  168. P. Norouzzadeh, W. Dullaert, B. Rahmani, Physica A: Statistical Mechanics and its Applications 380, 333 (2007) [CrossRef] [Google Scholar]
  169. W. Mensi, A.K. Tiwari, S.M. Yoon, Physica A: Statistical Mechanics and its Applications 471, 135 (2017) [CrossRef] [Google Scholar]
  170. A.K. Tiwari, C.T. Albulescu, S.M. Yoon, Physica A: Statistical Mechanics and its Applications 483, 182 (2017) [CrossRef] [Google Scholar]
  171. Y. Wang, Y. Wei, C. Wu, Physica A: Statistical Mechanics and its Applications 390, 817 (2011) [CrossRef] [Google Scholar]
  172. L. Zunino, B. Tabak, A. Figliola, D. Pérez, M. Garavaglia, O. Rosso, Physica A: Statistical Mechanics and its Applications 387, 6558 (2008) [CrossRef] [Google Scholar]
  173. B. Podobnik, H.E. Stanley, Phys. Rev. Lett. 100, 084102 (2008) [CrossRef] [PubMed] [Google Scholar]
  174. W.X. Zhou, Physical Review E 77 (2008) [Google Scholar]
  175. N. Costa, C. Silva, P. Ferreira, International Journal of Financial Studies 7 (2019) [Google Scholar]
  176. X.Y. Qian, Y.M. Liu, Z.Q. Jiang, B. Podobnik, W.X. Zhou, H.E. Stanley, Phys. Rev. E 91, 062816 (2015) [CrossRef] [Google Scholar]
  177. Z.Q. Jiang, W.X. Zhou, Phys. Rev. E 84, 016106 (2011) [CrossRef] [Google Scholar]
  178. L. Kristoufek, EPL (Europhysics Letters) 95, 68001 (2011) [CrossRef] [Google Scholar]
  179. J. Wang, P. Shang, W. Ge, Fractals 20, 271 (2012) [CrossRef] [Google Scholar]
  180. J. Li, X. Lu, Y. Zhou, Physica A: Statistical Mechanics and its Applications 453, 131 (2016) [CrossRef] [Google Scholar]
  181. C. Xie, Y. Zhou, G. Wang, X. Yan, Fluctuation and Noise Letters 16, 1750004-226 (2017) [CrossRef] [Google Scholar]
  182. F. Ma, Y. Wei, D. Huang, Physica A: Statistical Mechanics and its Applications 392, 1659 (2013) [CrossRef] [Google Scholar]
  183. Y. Wang, Y. Wei, C. Wu, Physica A: Statistical Mechanics and its Applications 389, 5468 (2010) [CrossRef] [Google Scholar]
  184. P. Yue, H.C. Xu, W. Chen, X. Xiong, W.X. Zhou, Fractals 25, 1750041 (2017) [CrossRef] [Google Scholar]
  185. F. Ma, Y. Wei, D. Huang, L. Zhao, Physica A: Statistical Mechanics and its Applications 392, 5356 (2013) [CrossRef] [Google Scholar]
  186. F. Ma, Q. Zhang, C. Peng, Y. Wei, Physica A: Statistical Mechanics and its Applications 410, 154 (2014) [CrossRef] [Google Scholar]
  187. Y. Wang, Y. Wei, C. Wu, Physica A: Statistical Mechanics and its Applications 390, 864 (2011) [CrossRef] [Google Scholar]
  188. X. Zhuang, Y. Wei, F. Ma, Physica A: Statistical Mechanics and its Applications 430, 101 (2015) [CrossRef] [Google Scholar]
  189. X. Zhuang, Y. Wei, B. Zhang, Physica A: Statistical Mechanics and its Applications 399, 113 (2014) [CrossRef] [Google Scholar]
  190. L. Xinsheng, L. Jianfeng, Z. Ying, Q. Yubo, Physica A: Statistical Mechanics and its Applications 486, 168 (2017) [CrossRef] [Google Scholar]
  191. Z. Zhang, Y. Zhang, D. Shen, W. Zhang, Complexity 2018, 1 (2018) [CrossRef] [Google Scholar]
  192. Z. Zhang, Y. Zhang, D. Shen, W. Zhang, Physica A: Statistical Mechanics and its Applications 508, 67 (2018) [CrossRef] [Google Scholar]
  193. Z. Da, J. Engelberg, P. Gao, Review of Financial Studies 28, 1 (2015) [CrossRef] [Google Scholar]
  194. W. Zhang, P. Wang, X. Li, D. Shen, Journal of Behavioral and Experimental Finance 18, 50 (2018) [CrossRef] [Google Scholar]
  195. M. Gronwald, C. Sattarhof, Working Paper 5, University of Aberdeen Business School (2018) [Google Scholar]
  196. T. Takaishi, Physica A: Statistical Mechanics and its Applications 506, 507–519 (2018) [CrossRef] [Google Scholar]
  197. L. Kirichenko, V. Bulakh, T. Radivilova, 2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T) (2017) [Google Scholar]
  198. Y. Jiang, H. Nie, W. Ruan, Finance Research Letters 25, 280 (2018) [CrossRef] [Google Scholar]
  199. A. Sensoy, E. Hacihasanoglu, Energy Economics 46, 318 (2014) [CrossRef] [Google Scholar]
  200. K.H. Al-Yahyaee, W. Mensi, S.M. Yoon, Finance Research Letters 27, 228 (2018) [CrossRef] [Google Scholar]
  201. G. Gajardo, W.D. Kristjanpoller, M. Minutolo, Chaos, Solitons & Fractals 109, 195 (2018) [CrossRef] [Google Scholar]
  202. S. Lahmiri, S. Bekiros, A. Salvi, Chaos, Solitons & Fractals 107, 43 (2018) [CrossRef] [Google Scholar]
  203. W. Zhang, P. Wang, X. Li, D. Shen, Complexity 2018, 1 (2018) [CrossRef] [Google Scholar]
  204. A. Ganchuk, V. Derbentsev, V. Soloviev, Multifractal Properties of the Ukraine Stock Market (2006), arXiv:physics/0608009v1 [physics.data-an] [Google Scholar]
  205. K. Hu, P.C. Ivanov, Z. Chen, P. Carpena, H. Eugene Stanley, Phys. Rev. E 64, 011114 (2001) [CrossRef] [Google Scholar]
  206. Z. Chen, P.C. Ivanov, K. Hu, H.E. Stanley, Phys. Rev. E 65, 041107 (2002) [CrossRef] [Google Scholar]
  207. J.R. Thompson, J.R. Wilson, Mathematics and Computers in Simulation 126, 63 (2016) [CrossRef] [Google Scholar]
  208. D. Clark, L. Tarra, A. Berera, Phys. Rev. Fluids 5, 064608 (2020) [CrossRef] [Google Scholar]
  209. R. Engelken, F. Wolf, L.F. Abbott, Lyapunov spectra of chaotic recurrent neural networks (2020), arXiv:2006.02427 [nlin.CD] [Google Scholar]
  210. K. Krishnamurthy, T. Can, D.J. Schwab, Theory of gating in recurrent neural networks (2021), arXiv:2007.14823 [cond-mat.dis-nn] [Google Scholar]
  211. W.S. Lee, S. Flach, Deep learning of chaos classification (2020), arXiv:2004.10980 [cs.LG] [Google Scholar]
  212. M.B. Tayel, E.I. AlSaba, Robust and sensitive method of lyapunov exponent for heart rate variability (2015), arXiv:1508.00996 [cs.SY] [Google Scholar]
  213. H. De Thélin, T. Gauthier, G. Vigny, Bulletin of the London Mathematical Society (2020) [Google Scholar]
  214. M. Cencini, F. Cecconi, A. Vulpiani, Chaos: From Simple Models to Complex Systems, Vol. 17 of Series on Advances in Statistical Mechanics (2010) [Google Scholar]
  215. P. Grassberger, I. Procaccia, Phys. Rev. Lett. 50, 346 (1983) [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  216. J.C. Sprott, Chaos and Time-Series Analysis (Princeton University Press, 2001), ISBN 9780198508403 [Google Scholar]
  217. L.S. Young, Journal of Physics A: Mathematical and Theoretical 46, 254001 (2013) [CrossRef] [Google Scholar]
  218. C.J. Gavilán-Moreno, G. Espinosa-Paredes, Nuclear Engineering and Technology 48, 434 (2016) [CrossRef] [Google Scholar]
  219. A. Prieto-Guerrero, G. Espinosa-Paredes, Dynamics of BWRs and mathematical models (2019), pp. 193–268 [Google Scholar]
  220. D. Nychka, S. Ellner, A.R. Gallant, D. McCaffrey, Journal of the Royal Statistical Society: Series B (Methodological) 54, 399 (1992) [CrossRef] [Google Scholar]
  221. A. Wolf, J. Swift, H.L. Swinney, J. Vastano, Physica D: Nonlinear Phenomena 16, 285 (1985) [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  222. M. Sano, Y. Sawada, Phys. Rev. Lett. 55, 1082 (1985) [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  223. J.P. Eckmann, S.O. Kamphorst, D. Ruelle, S. Ciliberto, Phys. Rev. A 34, 4971 (1986) [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  224. M.T. Rosenstein, J.J. Collins, C.J. De Luca, Physica D: Nonlinear Phenomena 65, 117 (1993) [CrossRef] [MathSciNet] [Google Scholar]
  225. U. Parlitz, International Journal of Bifurcation and Chaos 02, 155 (1992) [CrossRef] [Google Scholar]
  226. M. Balcerzak, D. Pikunov, A. Dabrowski, Nonlinear Dynamics 94, 3053 (2018) [CrossRef] [Google Scholar]
  227. J. Gao, Y. Cao, W.W. Tung, J. Hu, Multiscale Analysis of Complex Time Series: Integration of Chaos and Random Fractal Theory, and Beyond (Wiley, 2007), ISBN 978-0-470-19164-4 [Google Scholar]
  228. J. Gao, J. Hu, W.W. Tung, Y. Zheng, Quantitative Finance 13, 265 (2013) [CrossRef] [Google Scholar]
  229. V. Soloviev, D. Chabanenko, S. I., Using the scaledependent Lyapunov exponent as a measure of complexity, in The banking system of Ukraine in the context of globalization of financial markets: proceedings of VII International scientific and practical conference, edited by M. Gedz (CHIBS UBS NBU, 2012), pp. 469–471 [Google Scholar]
  230. V. Soloviev, I. Stratiychuk, The problems of economics 2, 279 (2013) [Google Scholar]
  231. J.P. Eckmann, S.O. Kamphorst, D. Ruelle, Europhysics Letters (EPL) 4, 973 (1987) [NASA ADS] [CrossRef] [Google Scholar]
  232. J. Scheinkman, B. Lebaron, The Journal of Business 62, 311 (1989) [CrossRef] [Google Scholar]
  233. H.D.I. Abarbanel, R. Brown, J.J. Sidorowich, L.S. Tsimring, Rev. Mod. Phys. 65, 1331 (1993) [NASA ADS] [CrossRef] [Google Scholar]
  234. V.S. Kulkarni, Complexity, chaos, and the duflngoscillator model: An analysis of inventory fluctuations in markets (2013), arXiv:1308.1616 [q-fin.GN] [Google Scholar]
  235. O. Bajo-Rubio, F. Fernandez-Rodriguez, S. Sosvilla-Rivero, Economics Letters 39, 207 (1992) [CrossRef] [Google Scholar]
  236. W.D. Dechert, R. Gencay, Journal of Applied Econometrics 7, S41 (1992) [CrossRef] [Google Scholar]
  237. R. Gençay, Physica D: Nonlinear Phenomena 89, 261 (1996) [CrossRef] [Google Scholar]
  238. S. Shreemoyee, C. Vikhyat, Engineering and Technology International Journal of Economics and Management Engineering 13 (2019) [Google Scholar]
  239. S. Srinivasan, S. Prasad, S. Patil, G. Lazarou, J. Picone, Estimation of Lyapunov Spectra from a Time Series, in Proceedings of the IEEE SoutheastCon 2006 (2006), pp. 192–195 [CrossRef] [Google Scholar]
  240. L. Mastroeni, P. Vellucci, “Chaos” in energy and commodity markets: a controversial matter (2017), arXiv:1611.07432 [q-fin.ST] [Google Scholar]
  241. V. Plakandaras, R. Gupta, M.E. Wohar, Applied Economics 51, 4477 (2019) [CrossRef] [Google Scholar]
  242. G. Chakrabarti, C. Sen, Anatomy of Global Stock Market Crashes, Number 978-81-322-0463-3 in SpringerBriefs in Economics (Springer, 2012) [CrossRef] [Google Scholar]
  243. J. Liesen, V. Mehrmann, Linear Algebra, Springer Undergraduate Mathematics Series, 1st edn. (Springer, 2015) [CrossRef] [Google Scholar]
  244. C. Touzé, A. Chaigne, Acustica 86, 1 (2000) [Google Scholar]
  245. B. Podobnik, A. Valentincˇicˇ, D. Horvatic´, H.E. Stanley, Proceedings of the National Academy of Sciences of the United States of America 108, 17883—17888 (2011) [CrossRef] [Google Scholar]
  246. L. Bachelier, Annales scientifiques de l’École Normale Supérieure 3e série, 17, 21 (1900) [Google Scholar]
  247. X. Gabaix, P. Gopikrishnan, V. Plerou, H. Stanley, Nature 423, 267 (2003) [CrossRef] [PubMed] [Google Scholar]
  248. M. Kateregga, S. Mataramvura, D. Taylor, Cogent Economics & Finance 5, 1318813 (2017) [CrossRef] [Google Scholar]
  249. D. Kre˛z˙ołek, Studia Ekonomiczne 247, 57–68 (2015) [Google Scholar]
  250. T. Lux, D. Sornette, Journal of Money, Credit and Banking 34, 589 (2002) [CrossRef] [Google Scholar]
  251. Y. Malevergne, V. Pisarenko, D. Sornette, Quantitative Finance 5, 379 (2005) [CrossRef] [MathSciNet] [Google Scholar]
  252. Y. Malevergne, V. Pisarenko, D. Sornette, Phys. Rev. E 83, 036111 (2011) [CrossRef] [Google Scholar]
  253. N.N. Taleb, International Journal of Forecasting 36, 1228–1240 (2020) [CrossRef] [Google Scholar]
  254. N.N. Taleb, Y. Bar-Yam, P. Cirillo, On single point forecasts for fat-tailed variables (2020), arXiv:2007.16096 [physics.soc-ph] [Google Scholar]
  255. P. Gopikrishnan, M. Meyer, L. Amaral, H. Stanley, The European Physical Journal B 3, 139–140 (1998) [CrossRef] [EDP Sciences] [Google Scholar]
  256. P. Gopikrishnan, V. Plerou, L.A. Nunes Amaral, M. Meyer, H.E. Stanley, Phys. Rev. E 60, 5305 (1999) [CrossRef] [Google Scholar]
  257. B. Podobnik, D. Horvatic, A.M. Petersen, H.E. Stanley, Proceedings of the National Academy of Sciences of the United States of America 106, 22079—22084 (2009) [CrossRef] [Google Scholar]
  258. B. Podobnik, K. Matia, A. Chessa, P.C. Ivanov, Y. Lee, H.E. Stanley, Physica A Statistical Mechanics and its Applications 300, 300 (2001) [CrossRef] [Google Scholar]
  259. X. Gabaix, Annual Review of Economics 1, 255 (2009) [CrossRef] [Google Scholar]
  260. Z. Kostanjcˇar, B. Jeren, Advances in Complex Systems 16, 1350008 (2013) [CrossRef] [Google Scholar]
  261. A. Chakraborty, S. Easwaran, S. Sinha, Deviations from universality in the fluctuation behavior of a heterogeneous complex system reveal intrinsic properties of components: The case of the international currency market (2018), arXiv:1606.06111 [q-fin.ST] [Google Scholar]
  262. T. Takaishi, Recent scaling properties of bitcoin price returns (2020), arXiv:2009.06874 [q-fin.ST] [Google Scholar]
  263. S. Droz˙dz˙, R. Ge¸barowski, L. Minati, P. Os´wie¸cimka, M. Waa¸torek, Chaos: An Interdisciplinary Journal of Nonlinear Science 28, 071101 (2018) [CrossRef] [Google Scholar]
  264. T. Takaishi, PLOS ONE 16, e0246209 (2021) [CrossRef] [Google Scholar]
  265. S. Begušic´, Z. Kostanjcˇar, H. Eugene Stanley, B. Podobnik, Physica A: Statistical Mechanics and its Applications 510, 400 (2018) [CrossRef] [Google Scholar]
  266. B. Mandelbrot, The Journal of Business 36 (1963) [Google Scholar]
  267. P. Levy, Bulletin de la Société Mathématique de France 52, 49 (1924) [CrossRef] [Google Scholar]
  268. B. Mandelbrot, International Economic Review 1, 79 (1960) [CrossRef] [Google Scholar]
  269. E.S. Andersen, Mathematica Scandinavica 3, 185 (1955) [Google Scholar]
  270. I.A. Koutrouvelis, Journal of the American Statistical Association 75, 918 (1980) [CrossRef] [Google Scholar]
  271. B. Brorsen, S. Yang, Communications in Statistics Part B: Simulation and Computation 19, 1459 (1990) [CrossRef] [Google Scholar]
  272. J. Nolan, Maximum Likelihood Estimation and Diagnostics for Stable Distributions (Birkhäuser, Boston, MA, 2001), pp. 379–400 [Google Scholar]
  273. E.F. Fama, R. Roll, Journal of the American Statistical Association 66, 331 (1971) [CrossRef] [Google Scholar]
  274. J.H. McCulloch, Communications in Statistics Simulation and Computation 15, 1109 (1986) [CrossRef] [Google Scholar]
  275. X. Ma, C.L. Nikias, IEEE Transactions on Signal Processing 43, 2884 (1995) [CrossRef] [Google Scholar]
  276. M. Shao, C.L. Nikias, Proceedings of the IEEE 81, 986 (1993) [CrossRef] [Google Scholar]
  277. J.M. Nicolas, Traitement Du Signal 19, 139 (2002) [Google Scholar]
  278. E.E. Kuruoglu, IEEE Transactions on Signal Processing 49, 2192 (2001) [NASA ADS] [CrossRef] [Google Scholar]
  279. W.H. DuMouchel, The Annals of Statistics 1, 948 (1973) [CrossRef] [Google Scholar]
  280. V.M. Zolotarev, One-dimensional stable distributions, Vol. 65 of Translations of Mathematical Monographs (American Mathematical Society, Providence, RI, 1986), translated from the Russian by H. H. McFaden, Translation edited by Ben Silver [CrossRef] [Google Scholar]
  281. J.M. Chambers, C.L. Mallows, B.W. Stuck, Journal of the American Statistical Association 71, 340 (1976) [CrossRef] [MathSciNet] [Google Scholar]
  282. S. Bianchi, A. Pantanella, International Journal of Trade, Economics and Finance 2, 52 (2011) [CrossRef] [Google Scholar]
  283. V.I. Arnold, A. Avez, ZAMM Journal of Applied Mathematics and Mechanics /Zeitschrift für Angewandte Mathematik und Mechanik 50, 506 (1970) [Google Scholar]
  284. K. Umeno, Nonlinear Theory and Its Applications, IEICE 7, 14 (2016) [CrossRef] [Google Scholar]
  285. I.A. Koutrouvelis, Communications in Statistics Simulation and Computation 10, 17 (1981) [CrossRef] [Google Scholar]
  286. V. Soloviev, V. Solovieva, D. Chabanenko, in Contemporary concepts of forecasting the development of complex socio-economic systems, edited by O. Chernyak, P. Zakharchenko (FO-P Tkachuk O V, Berdyansk, 2014), pp. 257–264 [Google Scholar]
  287. H. Poincaré, The Three-Body Problem and the Equations of Dynamics, Astrophysics and Space Science Library, 1st edn. (Springer, Cham, 2017) [CrossRef] [Google Scholar]
  288. P. Faure, H. Korn, Physica D: Nonlinear Phenomena 122, 265 (1998) [CrossRef] [Google Scholar]
  289. M. Thiel, M. Romano, J. Kurths, Analytical Description of Recurrence Plots of White Noise and Chaotic Processes, in Applied Nonlinear Dynamics (2003), Vol. 11 [Google Scholar]
  290. M. Thiel, M. Romano, J. Kurths, R. Meucci, E. Allaria, F. Arecchi, Physica D: Nonlinear Phenomena 171, 138 (2002) [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  291. L. Charles, J. Webber, I. Cornel, M. Norbert, eds., Recurrence Plots and Their Quantifications: Expanding Horizons, Vol. 180 of Springer Proceedings in Physics (Springer, 2015) [Google Scholar]
  292. N. Marwan, M. Carmen Romano, M. Thiel, J. Kurths, Physics Reports 438, 237 (2007) [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  293. G. Jianbo, C. Huaqing, Physics Letters A 270, 75 (2000) [CrossRef] [Google Scholar]
  294. N. Marwan, N. Wessel, U. Meyerfeldt, A. Schirdewan, J. Kurths, Phys. Rev. E 66, 026702 (2002) [NASA ADS] [CrossRef] [Google Scholar]
  295. C.L. Webber, J.P. Zbilut, Journal of Applied Physiology 76, 965 (1994) [CrossRef] [Google Scholar]
  296. J.P. Zbilut, C.L. Webber, Physics Letters A 171, 199 (1992) [CrossRef] [Google Scholar]
  297. G. Corso, T.L. Prado, G.Z. dos S. Lima, S.R. Lopes, A novel entropy recurrence quantification analysis (2017), arXiv:1707.00944 [stat.OT] [Google Scholar]
  298. M.A. Little, P.E. McSharry, S.J. Roberts, D.A. Costello, I.M. Moroz, BioMedical Engineering OnLine 6, 23 (2007) [CrossRef] [PubMed] [Google Scholar]
  299. H. Rabarimanantsoa, L. Achour, C. Letellier, A. Cuvelier, J.F. Muir, Chaos: An Interdisciplinary Journal of Nonlinear Science 17, 013115 (2007) [CrossRef] [PubMed] [Google Scholar]
  300. S. Lopes, T. Prado, G. Corso, G. dos S. Lima, J. Kurths, Chaos, Solitons & Fractals 133, 109616 (2020) [CrossRef] [Google Scholar]
  301. A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.K. Peng, H.E. Stanley, Circulation 101, e215 (2000) [Google Scholar]
  302. M. Kirchner, P. Schubert, M. Liebherr, C.T. Haas, PLOS ONE 9, 1 (2014) [CrossRef] [PubMed] [Google Scholar]
  303. I. Prigogine, From Being to Becoming Time and Complexity in the Physical Sciences, 1st edn. (W.H. Freeman & Co, 1981) [Google Scholar]
  304. M. Costa, A.L. Goldberger, C.K. Peng, Phys. Rev. E 71, 021906 (2005) [CrossRef] [MathSciNet] [Google Scholar]
  305. V. Soloviev, O. Rybchinska, Bulletin of the Kryvyi Rih Economic Institute KEI 2, 52 (2010) [Google Scholar]
  306. S. Daw, C. Finney, M. Kennel, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics 62, 1912 (2000) [CrossRef] [Google Scholar]
  307. C. Diks, J. van Houwelingen, F. Takens, J. DeGoede, Physics Letters A 201, 221 (1995) [NASA ADS] [CrossRef] [Google Scholar]
  308. J. F. Donges, R. V. Donner, J. Kurths, EPL (Europhysics Letters) 102, 10004 (2013) [CrossRef] [EDP Sciences] [Google Scholar]
  309. P. Guzik, J. Piskorski, T. Krauze, A. Wykretowicz, H. Wysocki, Biomedizinische Technik. Biomedical engineering 51, 272 (2006) [CrossRef] [Google Scholar]
  310. M.B. Kennel, Phys. Rev. E 69, 056208 (2004) [CrossRef] [Google Scholar]
  311. L. Lacasa, A. Nuñez, E. Roldán, J.M.R. Parrondo, B. Luque, The European Physical Journal B 85 (2012) [CrossRef] [Google Scholar]
  312. A. Porta, S. Guzzetti, N. Montano, T. GnecchiRuscone, R. Furlan, A. Malliani, Time reversibility in short-term heart period variability, in Computers in Cardiology (2006), Vol. 33, pp. 77–80, 2006 Computers in Cardiology, CIC ; Conference date: 17-09-2006 Through 20-09-2006 [Google Scholar]
  313. M. Zanin, A. Rodríguez-González, E. Menasalvas Ruiz, D. Papo, Entropy 20 (2018) [Google Scholar]
  314. W. Yao, W. Yao, D. Yao, D. Guo, J. Wang, Applied Physics Letters 116, 014101 (2020) [CrossRef] [Google Scholar]
  315. J. Li, P. Shang, X. Zhang, Nonlinear Dynamics 96, 2637 (2019) [CrossRef] [Google Scholar]
  316. R. Flanagan, L. Lacasa, Physics Letters A 380, 1689 (2016) [CrossRef] [Google Scholar]
  317. J.A. Martín-Gonzalo, I. Pulido-Valdeolivas, Y. Wang, T. Wang, G. Chiclana-Actis, M.d.C. Algarra-Lucas, I. Palmí-Cortés, J. Fernández Travieso, M.D. Torrecillas-Narváez, A.A. Miralles-Martinez et al., Entropy 21, 868 (2019) [CrossRef] [Google Scholar]
  318. J.H. Martínez, J.L. Herrera-Diestra, M. Chavez, Chaos: An Interdisciplinary Journal of Nonlinear Science 28, 123111 (2018) [CrossRef] [Google Scholar]
  319. Y. Wenpo, J. Dai, M. Perc, J. Wang, D. Yao, D. Guo, Nonlinear Dynamics 100, 907 (2020) [CrossRef] [Google Scholar]
  320. W. Yao, W. Yao, J. Wang, Physics Letters A 383, 1764 (2019) [CrossRef] [Google Scholar]
  321. G.G. Malinetsky, Computer research and modeling 5, 315 (2013) [CrossRef] [Google Scholar]
  322. T.U. Grund, The Journal of Mathematical Sociology 37, 131 (2013) [CrossRef] [Google Scholar]
  323. R. Cohen, S. Havlin, Complex Networks: Structure, Robustness and Function (Cambridge University Press, 2010) [CrossRef] [Google Scholar]
  324. G. Bianconi, EPL (Europhysics Letters) 111, 56001 (2015) [CrossRef] [Google Scholar]
  325. S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, D.U. Hwang, Physics Reports 424, 175 (2006) [CrossRef] [MathSciNet] [Google Scholar]
  326. Dynamics of graph spectral entropy in financial crisis, in Socio-Economic Aspects of Economics and Managment (Aspekt Publishing of Budget Printing Cente, 2015), pp. 227–234 [Google Scholar]
  327. V. Soloviev, Bulletin of Cherkasy University 38, 67 (2015) [Google Scholar]
  328. V. Soloviev, V. Solovieva, A. Tuliakova, NeuroFuzzy Modeling Techniques in Economics 8, 3 (2019) [CrossRef] [Google Scholar]
  329. V. Soloviev, V. Solovieva, A. Tuliakova, A. Hostryk, L. Pichl, CEUR Workshop Proceedings 2713, 53 (2020) [Google Scholar]
  330. S. Boccaletti, G. Bianconi, R. Criado, C. del Genio, J. Gómez-Gardeñes, M. Romance, I. SendiñaNadal, Z. Wang, M. Zanin, Physics Reports 544, 1 (2014), the structure and dynamics of multilayer networks [CrossRef] [Google Scholar]
  331. M.C. Qian, Z.Q. Jiang, W.X. Zhou, Journal of Physics A: Mathematical and Theoretical 43, 335002 (2010) [CrossRef] [MathSciNet] [Google Scholar]
  332. J. Birch, Ph.D. thesis (2015) [Google Scholar]
  333. F. Liu, N. Wang, D. Wei, The Open Cybernetics & Systemics Journal 11, 36 (2017) [CrossRef] [Google Scholar]
  334. W. Yan, E. van Tuyll van Serooskerken, PLOS ONE 10, 1 (2015) [CrossRef] [Google Scholar]
  335. A. Johansen, O. Ledoit, D. Sornette, International Journal of Theoretical and Applied Finance 03, 219 (2000) [CrossRef] [Google Scholar]
  336. M.D. Vamvakaris, A.A. Pantelous, K.M. Zuev, Physica A: Statistical Mechanics and its Applications 497, 41 (2018) [CrossRef] [Google Scholar]
  337. M. Serafino, A. Gabrielli, G. Caldarelli, G. Cimini, Statistical validation of financial time series via visibility graph (2017), arXiv:1710.10980 [q-fin.RM] [Google Scholar]
  338. C. Coquidé, J. Lages, D.L. Shepelyansky, Lecture Notes in Business Information Processing p. 208–219 (2019) [CrossRef] [Google Scholar]
  339. T. Squartini, A. Gabrielli, D. Garlaschelli, T. Gili, A. Bifone, F. Caccioli, Complexity 2018, 1 (2018) [CrossRef] [Google Scholar]
  340. L. Lacasa, V. Nicosia, V. Latora, Scientific Reports 5 (2015) [CrossRef] [Google Scholar]
  341. L. Bargigli, G. di Iasio, L. Infante, F. Lillo, F. Pierobon, Quantitative Finance 15, 673 (2015) [CrossRef] [Google Scholar]
  342. S. Li, S. Wen, Complexity 2017, 1 (2017) [CrossRef] [Google Scholar]
  343. C. Stephen, Procedia Computer Science 53, 410 (2015) [CrossRef] [Google Scholar]
  344. R.V. Donner, M. Small, J.F. Donges, N. Marwan, Y. Zou, R. Xiang, J. Kurths, International Journal of Bifurcation and Chaos 21, 1019 (2011) [CrossRef] [Google Scholar]
  345. L. Lacasa, B. Luque, F. Ballesteros, J. Luque, J.C. Nuño, Proceedings of the National Academy of Sciences 105, 4972–4975 (2008) [CrossRef] [MathSciNet] [Google Scholar]
  346. V. Soloviev, A. Tuliakova, Neuro-fuzzy modeling technologies in economics 5, 152 (2016) [Google Scholar]
  347. J. Iacovacci, L. Lacasa, Physical Review E 94 (2016) [CrossRef] [Google Scholar]
  348. A. de la Concha, S. Martinez-Jaramillo, C. Carmona, in Complex Networks & Their Applications VI (Springer International Publishing, 2018), pp. 1135–1148 [CrossRef] [Google Scholar]
  349. G. Colangelo, F.M. Ciurana, L.C. Bianchet, R.J. Sewell, M.W. Mitchell, Nature 543, 525–528 (2017) [CrossRef] [Google Scholar]
  350. E.G. Hidalgo, Quantum econophysics (2006), arXiv:physics/0609245 [physics.soc-ph] [Google Scholar]
  351. V. Maslov, Math. Notes 72, 811 (2002) [CrossRef] [Google Scholar]
  352. V. Soloviev, O. Serdiuk, Bulletin of Cherkasy University 1, 3 (2009) [Google Scholar]
  353. E. Benítez Rodríguez, L. Aguilar, Scientific Reports 8, 4010 (2018) [CrossRef] [Google Scholar]
  354. L.A. Rozema, A. Darabi, D.H. Mahler, A. Hayat, Y. Soudagar, A.M. Steinberg, Phys. Rev. Lett. 109, 100404 (2012) [CrossRef] [PubMed] [Google Scholar]
  355. M. Berta, M. Christandl, R. Colbeck, J.M. Renes, R. Renner, Nature Physics 6, 659–662 (2010) [CrossRef] [Google Scholar]
  356. R. Prevedel, D.R. Hamel, R. Colbeck, K. Fisher, K.J. Resch, Nature Physics 7, 757–761 (2011) [CrossRef] [Google Scholar]
  357. L. Landau, E. Lifshitz, Quantum Mechanics: NonRelativistic Theory, Course of Theoretical Physics, 3rd edn. (Butterworth-Heinemann, 1981) [Google Scholar]
  358. V. Soloviev, Y. Romanenko, Economic analog of Heisenberg uncertainly principle and financial crisis, in System analysis and information technology : 19-th International conference (ESC “IASA” NTUU “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine, 2017), SAIT 2017, pp. 32–33 [Google Scholar]
  359. V. Soloviev, S. Yevtushenko, V. Batareyev, CEUR Workshop Proceedings 2546, 87 (2019) [Google Scholar]
  360. S. Drozdz, J. Kwapien, P. Oswiecimka, Empirics versus rmt in financial cross-correlations (2007), arXiv:0711.0644 [q-fin.ST] [Google Scholar]
  361. F.J. Dyson, Journal of Mathematical Physics 3, 140 (1962) [CrossRef] [Google Scholar]
  362. E.P. Wigner, Annals of Mathematics 53, 36 (1951) [CrossRef] [Google Scholar]
  363. P.W. Anderson, Phys. Rev. 109, 1492 (1958) [Google Scholar]
  364. A. Lipton, A. Sardon, F. Schär, C. Schüpbach, From tether to libra: Stablecoins, digital currency and the future of money (2020), arXiv:2005.12949 [cs.CY] [Google Scholar]
  365. C. Karmakar, A. Khandoker, J. Gubbi, M. Palaniswami, Modified Ehlers’ index for improved detection of heart rate asymmetry in Poincaré plot, in 36th Annual Computers in Cardiology Conference (CinC) (IEEE, 2009), Vol. 36, pp. 169–172 [Google Scholar]
  366. A. Porta, S. Guzzetti, N. Montano, T. GnecchiRuscone, R. Furlan, A. Malliani, Time reversibility in short-term heart period variability, in 2006 Computers in Cardiology (IEEE, 2006), Vol. 2006, pp. 77–80 [Google Scholar]
  367. A. Puglisi, D. Villamaina, EPL 88, 30004 (2009) [CrossRef] [EDP Sciences] [Google Scholar]
  368. A. Abhishta, R. Joosten, S. Dragomiretskiy, L. Nieuwenhuis, Impact of Successful DDoS Attacks on a Major Crypto-Currency Exchange, in 2019 27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) (IEEE, United States, 2019), pp. 379–384 [Google Scholar]
  369. A.A. Kochkarov, S.D. Osipovich, R.A. Kochkarov, Revista ESPACIOS 41, 29 (2020) [Google Scholar]
  370. M. Conti, E. Sandeep Kumar, C. Lal, S. Ruj, IEEE Communications Surveys & Tutorials 20, 3416–3452 (2018) [CrossRef] [Google Scholar]
  371. M. Mirkin, Y. Ji, J. Pang, A. Klages-Mundt, I. Eyal, A. Juels, Bdos: Blockchain denial of service (2020), arXiv:1912.07497 [cs.CR] [Google Scholar]
  372. M. Vasek, M. Thornton, T. Moore, Empirical Analysis of Denial-of-Service Attacks in the Bitcoin Ecosystem, in Financial Cryptography and Data Security. FC 2014. Lecture Notes in Computer Science, edited by R. Böhme, M. Brenner, T. Moore, M. Smith (Springer, 2014), Vol. 8438 of FC 2014, pp. 57–71 [Google Scholar]
  373. U. Hacioglu, ed., Blockchain Economics and Financial Market Innovation, Contributions to Economics, 1st edn. (Springer, 2019), ISBN 978-3-03025275-5 [Google Scholar]
  374. S. Nakamoto, Bitcoin: A peer-to-peer electronic cash system (2009), http://www.bitcoin.org/bitcoin.pdf [Google Scholar]
  375. D. Aggarwal, G. Brennen, T. Lee, M. Santha, M. Tomamichel, Ledger 3 (2018) [CrossRef] [Google Scholar]
  376. D. Sapaev, D. Bulychkov, F. Ablayev, A. Vasiliev, M. Ziatdinov, Quantum-assisted blockchain (2018), arXiv:1802.06763 [quant-ph] [Google Scholar]
  377. O. Sattath, International Journal of Information Security 19, 291–302 (2020) [CrossRef] [Google Scholar]
  378. L. Tessler, T. Byrnes, Bitcoin and quantum computing (2018), arXiv:1711.04235 [quant-ph] [Google Scholar]
  379. L. Alessandretti, A. ElBahrawy, L.M. Aiello, A. Baronchelli, Complexity 2018, 1–16 (2018) [CrossRef] [Google Scholar]
  380. N. Gandal, H. Halaburda, Games 7 (2016) [CrossRef] [Google Scholar]
  381. T. Guo, A. Bifet, N. Antulov-Fantulin, 2018 IEEE International Conference on Data Mining (ICDM) (2018) [Google Scholar]
  382. H. Jang, J. Lee, IEEE Access 6, 5427 (2018) [Google Scholar]
  383. O. Sattarov, A. Muminov, C.W. Lee, H.K. Kang, R. Oh, J. Ahn, H.J. Oh, H.S. Jeon, Applied Sciences 10 (2020) [Google Scholar]
  384. D. Zhao, A. Rinaldo, C. Brookins, Cryptocurrency price prediction and trading strategies using support vector machines (2019), arXiv:1911.11819 [q-fin.TR] [Google Scholar]
  385. T.R. Li, A.S. Chamrajnagar, X.R. Fong, N.R. Rizik, F. Fu, Frontiers in Physics 7, 98 (2019) [CrossRef] [Google Scholar]
  386. W. Wei, Q. Zhang, L. Liu, Bitcoin transaction forecasting with deep network representation learning (2020), arXiv:2007.07993 [cs.SI] [Google Scholar]
  387. A.H.A. Othman, S. Kassim, R.B. Rosman, N.H.B. Redzuan, Journal of Revenue and Pricing Management 19, 314 (2020) [CrossRef] [Google Scholar]
  388. S. McNally, J. Roche, S. Caton, Predicting the Price of Bitcoin Using Machine Learning, in 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) (2018), pp. 339–343 [CrossRef] [Google Scholar]
  389. S.M. Raju, A.M. Tarif, Real-time prediction of bitcoin price using machine learning tech- niques and public sentiment analysis (2020), arXiv:2006.14473 [q-fin.ST] [Google Scholar]
  390. Hua Yiqing, E3S Web Conf. 218, 01050 (2020) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.