DSpace Home
→
Монографії
→
Фізико-математичний факультет
→
Кафедра фізики та методики її навчання (монографії)
→
View Item

Crisis Phenomena Monitoring and Prevention in Complex Socio-Economic Systems

Soloviev, Vladimir; Bielinskyi, Andrii; Matviychuk, Andriy; Соловйов, Володимир Миколайович; Бєлінський, Андрій Олександрович; Матвійчук, Андрій Вікторович

URI: https://butman2099.github.io/Complex-systems-book/
http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/10938

Date: 2024-11-02

Abstract:

This monograph presents research findings on the dynamic and structural characteristics of financial and economic systems, grounded in the principles of complex systems theory. For this purpose, the study employs methodologies such as recurrence analysis, entropy-based techniques, network science, etc. Significant attention is devoted to modeling critical phenomena within financial and economic systems. The investigation delves into the peculiarities of the collective dynamics of complex systems during periods of crisis and recovery. Special emphasis is placed on the identification and construction of indicators of pre-crisis states through advanced processing of time series data. This monograph will be valuable to a broad audience interested in the further development and practical application of interdisciplinary fields such as synergetics and econophysics, including specialists in economic and mathematical modeling, as well as graduate and undergraduate students.

Description:

A. A. Ganchuk, V. N. Soloviev, D. Chabanenko, Forecasting Methods: A Study Guide (Cherkasy: Brama-Ukraine, 2012). A. Barrat and M. Weigt, On the Properties of Small-World Network Models, The European Physical Journal B-Condensed Matter and Complex Systems 13, 547 (2000). A. Berman and R. J. Plemmons, Nonnegative Matrices in the Mathematical Sciences (Society for Industrial; Applied Mathematics, 1994). A. Bielinskyi, S. Semerikov, O. Serdyuk, V. Solovieva, V. N. Soloviev, and L. Pichl, Econophysics of Sustainability Indices, in Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, edited by A. Kiv, Vol. 2713 (CEUR-WS.org, 2020), pp. 372–392. A. Bielinskyi, V. Soloviev, V. Solovieva, A. Matviychuk, and S. Semerikov, The Analysis of Multifractal Cross-Correlation Connectedness Between Bitcoin and the Stock Market, in Information Technology for Education, Science, and Technics, edited by E. Faure, O. Danchenko, M. Bondarenko, Y. Tryus, C. Bazilo, and G. Zaspa (Springer Nature Switzerland, Cham, 2023), pp. 323–345. A. Faini, G. Parati, and P. Castiglioni, Multiscale Assessment of the Degree of Multifractality for Physiological Time Series, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, 20200254 (2021). A. Kalauzi, T. Bojić, and L. Rakić, Extracting Complexity Waveforms from One-Dimensional Signals, Nonlinear Biomedical Physics 3, 1 (2009). A. Kasprzak, R. Kutner, J. Perelló, and J. Masoliver, Higher-Order Phase Transitions on Financial Markets, The European Physical Journal B: Condensed Matter and Complex Systems 76, 513 (2010). A. Kiv, A. Bryukhanov, V. Soloviev, A. Bielinskyi, T. Kavetskyy, D. Dyachok, I. Donchev, and V. Lukashin, Complex Network Methods for Plastic Deformation Dynamics in Metals, Dynamics 3, 34 (2023). A. Lempel and J. Ziv, On the Complexity of Finite Sequences, IEEE Transactions on Information Theory 22, 75 (1976). A. Merchant et al. Scaling deep learning for materials discovery, Nature 624, 80-90 (2023). A. N. Kolmogorov, Three Approaches to the Quantitative Definition of Information, International Journal of Computer Mathematics 2, 157 (1968). A. O. Bielinskyi and V. N. Soloviev, Complex Network Precursors of Crashes and Critical Events in the Cryptocurrency Market, in Proceedings of St Student Workshop on Computer Science and Software Engineering, CS and SE@SW 2018, Kryvyi Rih, Ukraine, November 30, 2018, edited by S. O. Semerikov, A. M. Striuk, V. N. Soloviev, and A. E. Kiv, Vol. 2292 (CEUR-WS.org, 2028), pp. 37–45. A. O. Bielinskyi, A. V. Matviychuk, O. A. Serdyuk, S. O. Semerikov, V. V. Solovieva, and V. N. Soloviev, Correlational and Non-Extensive Nature of Carbon Dioxide Pricing Market, in ICTERI 2021 Workshops, edited by O. Ignatenko, V. Kharchenko, V. Kobets, H. Kravtsov, Y. Tarasich, V. Ermolayev, D. Esteban, V. Yakovyna, and A. Spivakovsky, Vol. 1635 (Springer International Publishing, Cham, 2022), pp. 183–199. A. O. Bielinskyi, O. A. Serdyuk, S. O. Semerikov, and V. N. Soloviev, Econophysics of Cryptocurrency Crashes: A Systematic Review, in Proceedings of the Selected and Revised Papers of 9th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2021), Odessa, Ukraine, May 26-28, 2021, edited by A. E. Kiv, V. N. Soloviev, and S. O. Semerikov, Vol. 3048 (CEUR-WS.org, 2021), pp. 31–133. A. O. Bielinskyi, V. N. Soloviev, S. O. Semerikov, and V. V. Solovieva, Identifying Stock Market Crashes by Fuzzy Measures of Complexity, Neuro-Fuzzy Modeling Techniques in Economics 10, 3 (2021). A. O. Bielinskyi, V. N. Soloviev, S. V. Hushko, A. E. Kiv, and A. V. Matviychuk, High-Order Network Analysis for Financial Crash Identification, in Proceedings of the Selected and Revised Papers of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2022), Virtual Event, Kryvyi Rih, Ukraine, November 17-18, 2022, edited by H. B. Danylchuk and S. O. Semerikov, Vol. 3465 (CEUR-WS.org, 2022), pp. 132–149. A. O. Bielinskyi, V. N. Soloviev, V. Solovieva, S. O. Semerikov, and M. A. Radin, Recurrence Quantification Analysis of Energy Market Crises: A Nonlinear Approach to Risk Management, in Proceedings of the Selected and Revised Papers of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2022), Virtual Event, Kryvyi Rih, Ukraine, November 17-18, 2022, edited by H. B. Danylchuk and S. O. Semerikov, Vol. 3465 (CEUR-WS.org, 2022), pp. 110–131. A. Orozco-Duque, D. Novak, V. Kremen, and J. Bustamante, Multifractal Analysis for Grading Complex Fractionated Electrograms in Atrial Fibrillation, Physiological Measurement 36, 2269 (2015). A. Petrosian, Kolmogorov Complexity of Finite Sequences and Recognition of Different Preictal EEG Patterns, in Proceedings Eighth IEEE Symposium on Computer-Based Medical Systems (1995), pp. 212–217. A. Prieto-Guerrero and G. Espinosa-Paredes, Dynamics of BWRs and Mathematical Models, in Linear and Non-Linear Stability Analysis in Boiling Water Reactors, edited by A. Prieto-Guerrero and G. Espinosa-Paredes (Woodhead Publishing, 2019), pp. 193–268. A. Tomashin, G. Leonardi, and S. Wallot, Four Methods to Distinguish Between Fractal Dimensions in Time Series Through Recurrence Quantification Analysis, Entropy 24, (2022). A. Vulpiani, Lewis Fry Richardson: Scientist, Visionary and Pacifist, Lettera Matematica 2, 121 (2014). A. Wolf, J. B. Swift, H. L. Swinney, and J. A. Vastano, Determining Lyapunov Exponents from a Time Series, Physica D: Nonlinear Phenomena 16, 285 (1985). B. B. Mandelbrot and J. A. Wheeler, The Fractal Geometry of Nature, American Journal of Physics 51, 286 (1983). B. B. Mandelbrot, C. J. G. Evertsz, and Y. Hayakawa, Exactly Self-Similar Left-Sided Multifractal Measures, Phys. Rev. A 42, 4528 (1990). B. Hayes, Computing Science: Statistics of Deadly Quarrels, American Scientist 90, 10 (2002). B. Hjorth, EEG Analysis Based on Time Domain Properties, Electroencephalography and Clinical Neurophysiology 29, 306 (1970). B. K. Hillen, G. T. Yamaguchi, J. J. Abbas, and R. Jung, Joint-Specific Changes in Locomotor Complexity in the Absence of Muscle Atrophy Following Incomplete Spinal Cord Injury, Journal of NeuroEngineering and Rehabilitation 10, 1 (2013). B. Luque, L. Lacasa, F. Ballesteros, and J. Luque, Horizontal Visibility Graphs: Exact Results for Random Time Series, Phys. Rev. E 80, 046103 (2009). B. Mandelbrot, How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension, Science 156, 636 (1967). C. Anteneodo and C. Tsallis, Breakdown of Exponential Sensitivity to Initial Conditions: Role of the Range of Interactions, Phys. Rev. Lett. 80, 5313 (1998). C. Bandt and B. Pompe, Permutation Entropy: A Natural Complexity Measure for Time Series, Phys. Rev. Lett. 88, 174102 (2002). C. Berge, Théorie Des Graphes Et Ses Applications (Dunod, 1958). C. E. Shannon, A Mathematical Theory of Communication, Bell System Technical Journal 27, 379 (1948). C. F. Vega and J. Noel, Parameters Analyzed of Higuchi’s Fractal Dimension for EEG Brain Signals, in 2015 Signal Processing Symposium (SPSympo) (2015), pp. 1–5. C. Goh, B. Hamadicharef, G. T. Henderson, and E. C. Ifeachor, Comparison of Fractal Dimension Algorithms for the Computation of EEG Biomarkers for Dementia, in 2nd International Conference on Computational Intelligence in Medicine and Healthcare (CIMED2005) (Professor José Manuel Fonseca, UNINOVA, Portugal, Lisbon, Portugal, 2005). C. J. Gavilán-Moreno and G. Espinosa-Paredes, Using Largest Lyapunov Exponent to Confirm the Intrinsic Stability of Boiling Water Reactors, Nuclear Engineering and Technology 48, 434 (2016). C. L. Webber and J. P. Zbilut, Dynamical Assessment of Physiological Systems and States Using Recurrence Plot Strategies, Journal of Applied Physiology 76, 965 (1994). C. Sevcik, A Procedure to Estimate the Fractal Dimension of Waveforms, (2010). C. Taufemback, R. Giglio, and S. D. Silva, Algorithmic complexity theory detects decreases in the relative efficiency of stock markets in the aftermath of the 2008 financial crisis, Economics Bulletin 31, 1631 (2011). C. Tsallis, Beyond Boltzmann–Gibbs–Shannon in Physics and Elsewhere, Entropy 21, (2019). C. Tsallis, Dynamical Scenario for Nonextensive Statistical Mechanics, Physica A: Statistical Mechanics and Its Applications 340, 1 (2004). C. Tsallis, Economics and Finance: Q-Statistical Stylized Features Galore, Entropy 19, (2017). C. Tsallis, M. Gell-Mann, and Y. Sato, Asymptotically Scale-Invariant Occupancy of Phase Space Makes the Entropy S_q Extensive, Proceedings of the National Academy of Sciences 102, 15377 (2005). C. Tsallis, Possible Generalization of Boltzmann-Gibbs Statistics, Journal of Statistical Physics 52, 479 (1988). C. Tsallis, Some Open Problems in Nonextensive Statistical Mechanics, International Journal of Bifurcation and Chaos 22, 1230030 (2012). C.-K. Peng, S. Havlin, H. E. Stanley, and A. L. Goldberger, Quantification of Scaling Exponents and Crossover Phenomena in Nonstationary Heartbeat Time Series, Chaos: An Interdisciplinary Journal of Nonlinear Science 5, 82 (1995). C.-K. Peng, S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, and A. L. Goldberger, Mosaic Organization of DNA Nucleotides, Phys. Rev. E 49, 1685 (1994). D. G. Bonchev, Information Theoretic Complexity Measures, in Encyclopedia of Complexity and Systems Science, edited by R. A. Meyers (Springer New York, New York, NY, 2009), pp. 4820–4839. D. J. Watts and S. H. Strogatz, Collective Dynamics of ’Small-World’ Networks, Nature 393, 440 (1998). D. M. Cvetkovic, M. Doob, and H. Sachs, Spectra of Graphs. Theory and Application (Academic Press, 1980). D. Nychka, S. Ellner, A. R. Gallant, and D. McCaffrey, Finding Chaos in Noisy Systems, Journal of the Royal Statistical Society. Series B (Methodological) 54, 399 (1992). D. Stosic, D. Stosic, T. B. Ludermir, and T. Stosic, Nonextensive Triplets in Cryptocurrency Exchanges, Physica A: Statistical Mechanics and Its Applications 505, 1069 (2018). Derbentsev V. D., Serdyuk O. A., Soloviev V. N., Sharapov O. D., Synergetic and econophysical methods of studying the dynamic and structural characteristics of economic systems: a monograph (Cherkasy: Brama-Ukraine, 2010). E. A. Ihlen, Introduction to Multifractal Detrended Fluctuation Analysis in Matlab, Frontiers in Physiology 3, (2012). E. Canessa, Multifractality in Time Series, Journal of Physics A: Mathematical and General 33, 3637 (2000). E. Estevez-Rams, R. Lora Serrano, B. Aragón Fernández, and I. Brito Reyes, On the non-randomness of maximum Lempel Ziv complexity sequences of finite size, Chaos: An Interdisciplinary Journal of Nonlinear Science 23, 023118 (2013). E. Estrada, Spectral Scaling and Good Expansion Properties in Complex Networks, Europhysics Letters 73, 649 (2006). E. G. Pavlos, O. E. Malandraki, O. V. Khabarova, L. P. Karakatsanis, G. P. Pavlos, and G. Livadiotis, Non-Extensive Statistical Analysis of Energetic Particle Flux Enhancements Caused by the Interplanetary Coronal Mass Ejection-Heliospheric Current Sheet Interaction, Entropy 21, (2019). E. Maiorino, L. Livi, A. Giuliani, A. Sadeghian, and A. Rizzi, Multifractal Characterization of Protein Contact Networks, Physica A: Statistical Mechanics and Its Applications 428, 302 (2015). F. Hasselman, When the Blind Curve Is Finite: Dimension Estimation and Model Inference Based on Empirical Waveforms, Frontiers in Physiology 4, (2013). F. Liao and Y.-K. Jan, Using Multifractal Detrended Fluctuation Analysis to Assess Sacral Skin Blood Flow Oscillations in People with Spinal Cord Injury, The Journal of Rehabilitation Research and Development 48, 787 (2011). F. Mormann, T. Kreuz, C. Rieke, R. G. Andrzejak, A. Kraskov, P. David, C. E. Elger, and K. Lehnertz, On the Predictability of Epileptic Seizures, Clinical Neurophysiology 116, 569 (2005). F. R. K. Chung, Spectral Graph Theory (American Mathematical Society, 1997). F. Takens, Detecting Strange Attractors in Turbulence, in Dynamical Systems and Turbulence, Warwick 1980, edited by D. Rand and L.-S. Young (Springer Berlin Heidelberg, Berlin, Heidelberg, 1981), pp. 366–381. G. Bianconi et al., Complex systems in the spotlight: next steps after the 2021 Nobel Prize in Physics, J. Phys. Complex. 4, 010201 (2023). G. Bounova and O. de Weck, Overview of Metrics and Their Correlation Patterns for Multiple-Metric Topology Analysis on Heterogeneous Graph Ensembles, Phys. Rev. E 85, 016117 (2012). G. L. Ferri, M. F. Reynoso Savio, and A. Plastino, Tsallis’ q-Triplet and the Ozone Layer, Physica A: Statistical Mechanics and Its Applications 389, 1829 (2010). G. Nicolis, I. Prigogine, W. H. Freeman, and Company, Exploring Complexity: An Introduction (W.H. Freeman, 1989). G. Pavlos, A. Iliopoulos, L. Karakatsanis, M. Xenakis, and E. Pavlos, Complexity of Economical Systems., Journal of Engineering Science & Technology Review 8, (2015). G. R. Jafari, P. Pedram, and L. Hedayatifar, Erratum: Long-Range Correlation and Multifractality in Bach’s Inventions Pitches, Journal of Statistical Mechanics: Theory and Experiment 2012, E03001 (2012). H. D. I. Abarbanel, R. Brown, J. J. Sidorowich, and L. Sh. Tsimring, The Analysis of Observed Chaotic Data in Physical Systems, Rev. Mod. Phys. 65, 1331 (1993). H. E. Hurst, A Suggested Statistical Model of Some Time Series Which Occur in Nature, Nature 180, 494 (1957). H. E. Hurst, Long-Term Storage Capacity of Reservoirs, Transactions of the American Society of Civil Engineers 116, 770 (1951). H. F. Jelinek, N. Elston, and B. Zietsch, Fractal Analysis: Pitfalls and Revelations in Neuroscience, in Fractals in Biology and Medicine, edited by G. A. Losa, D. Merlini, T. F. Nonnenmacher, and E. R. Weibel (Birkhäuser Basel, Basel, 2005), pp. 85–94. H. Kantz and T. Schreiber, Nonlinear Time Series Analysis (Cambridge University Press, 2004). H. Steinhaus, Length, Shape and Area, in Colloquium Mathematicum, Vol. 3 (Polska Akademia Nauk. Instytut Matematyczny PAN, 1954), pp. 1–13. H.-B. Xie, W.-X. He, and H. Liu, Measuring Time Series Regularity Using Nonlinear Similarity-Based Sample Entropy, Physics Letters A 372, 7140 (2008). I. Gutman, The Energy of a Graph, Ber. Math.— Statist. Sekt. Forschungsz 103, 1-22. (1978). I. J. Schoenberg, Publications of Edmund Landau, in Number Theory and Analysis: A Collection of Papers in Honor of Edmund Landau (1877–1938), edited by P. Turán (Springer US, Boston, MA, 1969), pp. 335–355. I. T. Pedron, Correlation and Multifractality in Climatological Time Series, Journal of Physics: Conference Series 246, 012034 (2010). I. V. Bezsudnov and A. A. Snarskii, From the Time Series to the Complex Networks: The Parametric Natural Visibility Graph, Physica A: Statistical Mechanics and Its Applications 414, 53 (2014). J. C. Crepeau and L. K. Isaacson, Spectral Entropy Measurements of Coherent Structures in am Evolving Shear Layer, Journal of Non-Equilibrium Thermodynamics 16, 137 (1991). J. P. Zbilut and C. L. Webber, Embeddings and Delays as Derived from Quantification of Recurrence Plots, Physics Letters A 171, 199 (1992). J. S. Richman and J. R. Moorman, Physiological Time-Series Analysis Using Approximate Entropy and Sample Entropy, American Journal of Physiology-Heart and Circulatory Physiology 278, H2039 (2000). J. W. Kantelhardt, E. Koscielny-Bunde, H. H. A. Rego, S. Havlin, and A. Bunde, Detecting Long-Range Correlations with Detrended Fluctuation Analysis, Physica A: Statistical Mechanics and Its Applications 295, 441 (2001). J. W. Kantelhardt, Fractal and Multifractal Time Series, in Mathematics of Complexity and Dynamical Systems, edited by R. A. Meyers (Springer New York, New York, NY, 2011), pp. 463–487. J. W. Kantelhardt, S. A. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, and H. E. Stanley, Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series, Physica A: Statistical Mechanics and Its Applications 316, 87 (2002). J. Wu, M. Barahona, Y.-J. Tan, and H.-Z. Deng, Spectral Measure of Structural Robustness in Complex Networks, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 41, 1244 (2011). J.-L. Blanc, L. Pezard, and A. Lesne, Delay Independence of Mutual-Information Rate of Two Symbolic Sequences, Phys. Rev. E 84, 036214 (2011). J.-P. Eckmann and D. Ruelle, Ergodic Theory of Chaos and Strange Attractors, Rev. Mod. Phys. 57, 617 (1985). J.-P. Eckmann, S. O. Kamphorst, and D. Ruelle, Recurrence Plots of Dynamical Systems, Europhysics Letters 4, 973 (1987). J.-P. Eckmann, S. O. Kamphorst, D. Ruelle, and S. Ciliberto, Liapunov Exponents from Time Series, Phys. Rev. A 34, 4971 (1986). K. Falconer, Fractal Geometry: Mathematical Foundations and Applications (John Wiley & Sons, 2003). K. Shockley and M. Riley, In Recurrence Quantification Analysis: Theory and Best Practices, 1st ed. (Springer, New York, 2015). L. Boltzmann, Weitere Studien über Das wärmegleichgewicht Unter Gasmolekülen, in Kinetische Theorie II: Irreversible Prozesse Einführung Und Originaltexte (Vieweg+Teubner Verlag, Wiesbaden, 1970), pp. 115–225. L. Lacasa, A. Nuñez, É. Roldán, J. M. R. Parrondo, and B. Luque, Time Series Irreversibility: A Visibility Graph Approach, The European Physical Journal B 85, (2012). L. Lacasa, B. Luque, F. Ballesteros, J. Luque, and J. C. Nuño, From Time Series to Complex Networks: The Visibility Graph, Proceedings of the National Academy of Sciences 105, 4972 (2008). L. T. Lui, G. Terrazas, H. Zenil, C. Alexander, and N. Krasnogor, Complexity Measurement Based on Information Theory and Kolmogorov Complexity, Artificial Life 21, 205 (2015). L. Telesca, V. Lapenna, and M. Macchiato, Multifractal Fluctuations in Seismic Interspike Series, Physica A: Statistical Mechanics and Its Applications 354, 629 (2005). M. Balcerzak, D. Pikunov, and A. Dabrowski, The Fastest, Simplified Method of Lyapunov Exponents Spectrum Estimation for Continuous-Time Dynamical Systems, Nonlinear Dynamics 94, 3053 (2018). M. Borowska, Multiscale Permutation Lempel–Ziv Complexity Measure for Biomedical Signal Analysis: Interpretation and Application to Focal EEG Signals, Entropy 23, (2021). M. D. Costa, C.-K. Peng, and A. L. Goldberger, Multiscale Analysis of Heart Rate Dynamics: Entropy and Time Irreversibility Measures, Cardiovascular Engineering 8, 88 (2008). M. Dai, C. Zhang, and D. Zhang, Multifractal and Singularity Analysis of Highway Volume Data, Physica A: Statistical Mechanics and Its Applications 407, 332 (2014). M. Dai, J. Hou, and D. Ye, Multifractal Detrended Fluctuation Analysis Based on Fractal Fitting: The Long-Range Correlation Detection Method for Highway Volume Data, Physica A: Statistical Mechanics and Its Applications 444, 722 (2016). M. E. J. Newman, Assortative Mixing in Networks, Phys. Rev. Lett. 89, 208701 (2002). M. G. Kendall, Further Contributions to the Theory of Paired Comparisons, Biometrics 11, 43 (1955). M. J. Katz, Fractals and the Analysis of Waveforms, Computers in Biology and Medicine 18, 145 (1988). M. Li and P. Vitányi, Preliminaries, in An Introduction to Kolmogorov Complexity and Its Applications (Springer New York, New York, NY, 2008), pp. 1–99. M. Rostaghi and H. Azami, Dispersion Entropy: A Measure for Time-Series Analysis, IEEE Signal Processing Letters 23, 610 (2016). M. S. Kanwal, J. A. Grochow, and N. Ay, Comparing Information-Theoretic Measures of Complexity in Boltzmann Machines, Entropy 19, (2017). M. S. Movahed, F. Ghasemi, S. Rahvar, and M. R. R. Tabar, Long-Range Correlation in Cosmic Microwave Background Radiation, Phys. Rev. E 84, 021103 (2011). M. Sano and Y. Sawada, Measurement of the Lyapunov Spectrum from a Chaotic Time Series, Phys. Rev. Lett. 55, 1082 (1985). M. T. Rosenstein, J. J. Collins, and C. J. De Luca, A Practical Method for Calculating Largest Lyapunov Exponents from Small Data Sets, Physica D: Nonlinear Phenomena 65, 117 (1993). M. Wątorek, S. Drożdż, J. Kwapień, L. Minati, P. Oświęcimka, and M. Stanuszek, Multiscale Characteristics of the Emerging Global Cryptocurrency Market, Physics Reports 901, 1 (2021). N. Biggs, Spectral Graph Theory (CBMS Regional Conference Series in Mathematics 92), Bulletin of the London Mathematical Society 30, 197 (1998). N. H. Packard, J. P. Crutchfield, J. D. Farmer, and R. S. Shaw, Geometry from a Time Series, Phys. Rev. Lett. 45, 712 (1980). N. Marwan, N. Wessel, U. Meyerfeldt, A. Schirdewan, and J. Kurths, Recurrence-Plot-Based Measures of Complexity and Their Application to Heart-Rate-Variability Data, Phys. Rev. E 66, 026702 (2002). P. Bonacich, Technique for Analyzing Overlapping Memberships, Sociological Methodology 4, 176 (1972). P. G. Lind, M. C. González, and H. J. Herrmann, Cycles and Clustering in Bipartite Networks, Phys. Rev. E 72, 056127 (2005). P. Grassberger and I. Procaccia, Characterization of Strange Attractors, Phys. Rev. Lett. 50, 346 (1983). P. Grassberger and I. Procaccia, Measuring the Strangeness of Strange Attractors, Physica D: Nonlinear Phenomena 9, 189 (1983). P. Grassberger, Generalized Dimensions of Strange Attractors, Physics Letters A 97, 227 (1983). P. H. Figueirêdo, E. Nogueira, M. A. Moret, and S. Coutinho, Multifractal Analysis of Polyalanines Time Series, Physica A: Statistical Mechanics and Its Applications 389, 2090 (2010). P. Holme, C. R. Edling, and F. Liljeros, Structure and Time Evolution of an Internet Dating Community, Social Networks 26, 155 (2004). P. Holme, F. Liljeros, C. R. Edling, and B. J. Kim, Network Bipartivity, Phys. Rev. E 68, 056107 (2003). P. Oświȩcimka, L. Livi, and S. Drożdż, Right-Side-Stretched Multifractal Spectra Indicate Small-Worldness in Networks, Communications in Nonlinear Science and Numerical Simulation 57, 231 (2018). P. W. Anderson, More is different, New Series 77, 393-396 (1972). P. Zhang, J. Wang, X. Li, M. Li, Z. Di, and Y. Fan, Clustering Coefficient and Community Structure of Bipartite Networks, Physica A: Statistical Mechanics and Its Applications 387, 6869 (2008). Q. Xuan, J. Zhou, K. Qiu, D. Xu, S. Zheng, and X. Yang, CLPVG: Circular limited penetrable visibility graph as a new network model for time series, Chaos: An Interdisciplinary Journal of Nonlinear Science 32, 013130 (2022). R. A. Fisher and E. J. Russell, On the Mathematical Foundations of Theoretical Statistics, Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character 222, 309 (1922). R. Clausius, T. A. Hirst, and J. Tyndall, The Mechanical Theory of Heat: With Its Applications to the Steam-Engine and to the Physical Properties of Bodies (J. Van Voorst, 1867). R. de Oliveira, S. Brito, L. da Silva, and C. Tsallis, Connecting Complex Networks to Nonadditive Entropies, Scientific Reports 11, 1130 (2021). R. Esteller, G. Vachtsevanos, J. Echauz, and B. Litt, A Comparison of Waveform Fractal Dimension Algorithms, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 48, 177 (2001). R. F. Voss, Fractals in Nature: From Characterization to Simulation, in The Science of Fractal Images, edited by H.-O. Peitgen and D. Saupe (Springer New York, New York, NY, 1988), pp. 21–70. R. Giglio and S. Da Silva, Ranking the Stocks Listed on Bovespa According to Their Relative Efficiency, MPRA Paper, University Library of Munich, Germany, 2009. R. Giglio, R. Matsushita, A. Figueiredo, I. Gleria, and S. D. Silva, Algorithmic Complexity Theory and the Relative Efficiency of Financial Markets, Europhysics Letters 84, 48005 (2008). R. Pastor-Satorras, A. Vázquez, and A. Vespignani, Dynamical and Correlation Properties of the Internet, Phys. Rev. Lett. 87, 258701 (2001). R. Rak, S. Drożdż, J. Kwapień, and P. Oświȩcimka, Detrended Cross-Correlations Between Returns, Volatility, Trading Activity, and Volume Traded for the Stock Market Companies, Europhysics Letters 112, 48001 (2015). R. V. Donner, M. Small, J. F. Donges, N. Marwan, Y. Zou, R. Xiang, and J. Kurths, Recurrence-Based Time Series Analysis by Means of Complex Network Methods, International Journal of Bifurcation and Chaos 21, 1019 (2011). S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang, Complex Networks: Structure and Dynamics, Physics Reports 424, 175 (2006). S. Butler, Interlacing for Weighted Graphs Using the Normalized Laplacian, Electronic Journal of Linear Algebra 16, 90 (2007). S. Drożdż and P. Oświȩcimka, Detecting and Interpreting Distortions in Hierarchical Organization of Complex Time Series, Phys. Rev. E 91, 030902 (2015). S. Drożdż, R. Kowalski, P. Oświȩcimka, R. Rak, and R. Gȩbarowski, Dynamical Variety of Shapes in Financial Multifractality, Complexity 2018, 1 (2018). S. Dutta, Multifractal Properties of ECG Patterns of Patients Suffering from Congestive Heart Failure, Journal of Statistical Mechanics: Theory and Experiment 2010, P12021 (2010). S. J. Roberts, W. Penny, and I. Rezek, Temporal and Spatial Complexity Measures for Electroencephalogram Based Brain-Computer Interfacing, Medical & Biological Engineering & Computing 37, 93 (1999). S. M. Pincus, Approximate Entropy as a Measure of System Complexity, Proceedings of the National Academy of Sciences 88, 2297 (1991). S. M. Pincus, I. M. Gladstone, and R. A. Ehrenkranz, A Regularity Statistic for Medical Data Analysis, Journal of Clinical Monitoring 7, 335 (1991). S. Maslov and K. Sneppen, Specificity and Stability in Topology of Protein Networks, Science 296, 910 (2002). S. Umarov, C. Tsallis, and S. Steinberg, On Aq-Central Limit Theorem Consistent with Nonextensive Statistical Mechanics, Milan Journal of Mathematics 76, 307 (2008). S. V. Bozhokin and D. A. Parshin, Fractals and Multifractals: Textbook (Scientific; Publishing Center “Regular; Chaotic Dynamics”, 2001). S. Zozor, P. Ravier, and O. Buttelli, On Lempel–Ziv Complexity for Multidimensional Data Analysis, Physica A: Statistical Mechanics and Its Applications 345, 285 (2005). T. C. Halsey, M. H. Jensen, L. P. Kadanoff, I. Procaccia, and B. I. Shraiman, Fractal Measures and Their Singularities: The Characterization of Strange Sets, Nuclear Physics B - Proceedings Supplements 2, 501 (1987). T. Gneiting, H. Ševčíková, and D. B. Percival, Estimators of Fractal Dimension: Assessing the Roughness of Time Series and Spatial Data, Statistical Science 27, 247 (2012). T. H. Wei, The Algebraic Foundations of Ranking Theory (University of Cambridge, 1952). T. Higuchi, Approach to an Irregular Time Series on the Basis of the Fractal Theory, Physica D: Nonlinear Phenomena 31, 277 (1988). T. Rawald, Scalable and Efficient Analysis of Large High-Dimensional Data Sets in the Context of Recurrence Analysis, PhD thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2018. T. T. Zhou, N. D. Jin, Z. K. Gao, and Y. B. Luo, Limited Penetrable Visibility Graph for Establishing Complex Network from Time Series, Acta Physica Sinica 61, 2012-3-030506 (2012). U. Parlitz, Identification of True and Spurious Lyapunov Exponents from Time Series, International Journal of Bifurcation and Chaos 02, 155 (1992). V. Latora and M. Marchiori, Efficient Behavior of Small-World Networks, Phys. Rev. Lett. 87, 198701 (2001). V. Marmelat, K. Torre, and D. Delignieres, Relative Roughness: An Index for Testing the Suitability of the Monofractal Model, Frontiers in Physiology 3, (2012). V. N. Soloviev and A. Belinskiy, Complex Systems Theory and Crashes of Cryptocurrency Market, in Information and Communication Technologies in Education, Research, and Industrial Applications, edited by V. Ermolayev, M. C. Suárez-Figueroa, V. Yakovyna, H. C. Mayr, M. Nikitchenko, and A. Spivakovsky (Springer International Publishing, Cham, 2019), pp. 276–297. V. N. Soloviev and A. Belinskyi, Methods of Nonlinear Dynamics and the Construction of Cryptocurrency Crisis Phenomena Precursors, in Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kyiv, Ukraine, May 14-17, 2018, edited by V. Ermolayev, M. C. Suárez-Figueroa, V. Yakovyna, V. S. Kharchenko, V. Kobets, H. Kravtsov, V. S. Peschanenko, Y. Prytula, M. S. Nikitchenko, and A. Spivakovsky, Vol. 2104 (CEUR-WS.org, 2018), pp. 116–127. V. N. Soloviev, A. Bielinskyi, and V. Solovieva, Entropy Analysis of Crisis Phenomena for DJIA Index, in Proceedings of the 15th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kherson, Ukraine, June 12-15, 2019, edited by V. Ermolayev, F. Mallet, V. Yakovyna, V. S. Kharchenko, V. Kobets, A. Kornilowicz, H. Kravtsov, M. S. Nikitchenko, S. Semerikov, and A. Spivakovsky, Vol. 2393 (CEUR-WS.org, 2019), pp. 434–449. V. N. Soloviev, A. Bielinskyi, O. Serdyuk, V. Solovieva, and S. Semerikov, Lyapunov Exponents as Indicators of the Stock Market Crashes, in Proceedings of the 16th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kharkiv, Ukraine, October 06-10, 2020, edited by O. Sokolov, G. Zholtkevych, V. Yakovyna, Y. Tarasich, V. Kharchenko, V. Kobets, O. Burov, S. Semerikov, and H. Kravtsov, Vol. 2732 (CEUR-WS.org, 2020), pp. 455–470. V. N. Soloviev, A. O. Bielinskyi, and N. A. Kharadzjan, Coverage of the Coronavirus Pandemic Through Entropy Measures, in 3rd Workshop for Young Scientists in Computer Science and Software Engineering (CS and SE and SW 2020), Kryvyi Rih, Ukraine, November 27, 2020, edited by A. E. Kiv, S. O. Semerikov, V. N. Soloviev, and A. M. Striuk, Vol. 2832 (CEUR-WS.org, 2021), pp. 24–42. V. N. Soloviev, A. O. Bielinskyi, Complex Systems Modeling in Python: A manual for self-study of the discipline (Cherkasy: O.M. Tretyakov, 2024). V. N. Soloviev, Mathematical economics: a study guide for self-study (B. Khmelnytsky ChNU Publishing House, 2008). V. N. Soloviev, O. A. Serdyuk, H. B. Danilchuk, Modeling of Complex Systems: A Study Guide for Independent Study of the Discipline (Cherkasy: Vovchok Publishing House, 2016). W. Chen, Z. Wang, H. Xie, and W. Yu, Characterization of Surface EMG Signal Based on Fuzzy Entropy, IEEE Transactions on Neural Systems and Rehabilitation Engineering 15, 266 (2007). W. Jun, M. Barahona, T. Yue-Jin, and D. Hong-Zhong, Natural Connectivity of Complex Networks, Chinese Physics Letters 27, 078902 (2010). X. Lan, H. Mo, S. Chen, Q. Liu, and Y. Deng, Fast transformation from time series to visibility graphs, Chaos: An Interdisciplinary Journal of Nonlinear Science 25, 083105 (2015). X. Sun, H. Chen, Z. Wu, and Y. Yuan, Multifractal Analysis of Hang Seng Index in Hong Kong Stock Market, Physica A: Statistical Mechanics and Its Applications 291, 553 (2001). Y. Bai, Z. Liang, and X. Li, A Permutation Lempel-Ziv Complexity Measure for EEG Analysis, Biomedical Signal Processing and Control 19, 102 (2015). Y. Holovatch, R. Kenna, S. Thurner, Complex systems: physics beyond physics, Eur. J. Phys. 38, 023002 (2017). Z. Chu, H. Guo, X. Zhou, Y. Wang, F. Yu, H. Chen, W. Xu, X. Lu, Q. Cui, L. Li, J. Zhou, Data-centric financial large language models. arXiv preprint arXiv:2310.17784 (2023). Z.-Q. Jiang, W.-J. Xie, and W.-X. Zhou, Testing the Weak-Form Efficiency of the WTI Crude Oil Futures Market, Physica A: Statistical Mechanics and Its Applications 405, 235 (2014). Z.-Q. Jiang, W.-J. Xie, W.-X. Zhou, and D. Sornette, Multifractal Analysis of Financial Markets: A Review, Reports on Progress in Physics 82, 125901 (2019).

Show full item record