Description:
1. Halvin, S., Cohen, R.: Complex networks. Structure, robustness and function. Cambridge
University Press, New York (2010)
2. Albert, R., Barabási, A.-L.: Statistical Mechanics of Complex Networks. Rev. Mod. Phys.
74, 47–97 (2002). doi:10.1103/RevModPhys.74.47
3. Newman, M., Barabási A.-L., Watts D.J.: The Structure and Dynamics of Networks.
Princeton University Press, Princeton (2006)
4. Newman, M.E.J.: The Structure and Function of Complex Networks. SIAM Reviews. 45(2),
167–256 (2003). doi:10.1137/S003614450342480
5. Nikolis, G., Prigogine, I.: Exploring Complexity: An Introduction. St. Martin’s Press, New
York (1989)
6. Andrews, B., Calder, M., Davis, R.A.: Maximum Likelihood Estimation for α-Stable
Autoregressive Processes. The Annals of Statistics. 37(4), 1946–1982 (2009).
doi:10.1214/08-AOS632
7. Shah, D., Zhang, K.: Bayesian regression and Bitcoin. In: 2014 52nd Annual Allerton
Conference on Communication, Control, and Computing (Allerton), Monticello, 30 Sept.-3
Oct. 2014. doi:10.1109/ALLERTON.2014.7028484
8. Chen, T., Guestrin, C.: XGBoost: A Scalable Tree Boosting System. In: KDD '16
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining, San Francisco, August 13-17, 2016, pp. 785-794 (2016).
doi:10.1145/2939672.2939785
9. Alessandretti, L., ElBahrawy, A., Aiello, L.M., Baronchelli, A.: Machine Learning the
Cryptocurrency Market. https://ssrn.com/abstract=3183792 (2018).
doi:10.2139/ssrn.3183792. Accessed 15 Sep 2018
10. Guo, T., Antulov-Fantulin, N.: An experimental study of Bitcoin fluctuation using machine
learning methods. arXiv:1802.04065v2 [stat.ML]. https://arxiv.org/pdf/1802.04065.pdf
(2018). Accessed 15 Sep 2018 11. Peng Y., Albuquerque, P.H.M., de Sá, J.M.C., Padula, A.J.A., Montenegro, M.R: The best
of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional
currencies with Support Vector Regression. Expert Systems with Applications. 97, 177–192
(2018). doi:10.1016/j.eswa.2017.12.004
12. Donier, J., Bouchaud J.-P.: Why Do Markets Crash? Bitcoin Data Offers Unprecedented
Insights. PLoS ONE 10(10): e0139356 (2015). doi:10.1371/journal.pone.0139356
13. Di Francesco Maesa, D., Marino, A., Ricci, L.: Data-driven analysis of Bitcoin properties:
exploiting the users graph. International Journal of Data Science and Analytics. 6(1), 63–80
(2018). doi:10.1007/s41060-017-0074-x
14. Bovet, A., Campajola, C., Lazo, J.F., Mottes, F., Pozzana, I., Restocchi, V., Saggese, P.,
Vallarano, N., Squartini, T., Tessone, C.J.: Network-based indicators of Bitcoin bubbles.
arXiv:1805.04460v1 [physics.soc-ph]. https://arxiv.org/pdf/1805.04460 (2018). Accessed
11 Sep 2018
15. Kondor, D., Csabai, I., Szüle, J., Pόsfai, M., Vattay, G.: Inferring the interplay of network
structure and market effects in Bitcoin. New Journal of Physics. 16, 125003 (2014).
doi:10.1088/1367-2630/16/12/125003
16. Wheatley, S., Sornette, D., Huber, T., Reppen, M., Gantner, R.N.: Are Bitcoin Bubbles
Predictable? Combining a Generalized Metcalfe’s Law and the LPPLS Model. Swiss
Finance Institute Research Paper No. 18-22. https://ssrn.com/abstract=3141050 (2018).
doi:10.2139/ssrn.3141050. Accessed 15 Sep 2018
17. Gerlach, J-C., Demos, G., Sornette, D.: Dissection of Bitcoin’s Multiscale Bubble History
from January 2012 to February 2018. arXiv:1804.06261v2 [econ.EM].
https://arxiv.org/pdf/1804.06261 (2018). Accessed 15 Sep 2018
18. Soloviev, V., Belinskij, A.: Methods of nonlinear dynamics and the construction of
cryptocurrency crisis phenomena precursors. In: Ermolayev, V., Suárez-Figueroa, M.C.,
Yakovyna, V., Kharchenko, V., Kobets, V., Kravtsov, H., Peschanenko, V., Prytula, Y.,
Nikitchenko, M., Spivakovsky, A. (eds.) 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. CEUR
Workshop Proceedings. 2014, 116–127. http://ceur-ws.org/Vol-2104/paper_175.pdf (2018).
Accessed 30 Sep 2018
19. Casey, M.B.: Speculative Bitcoin Adoption/Price Theory.
https://medium.com/@mcasey0827/speculative-bitcoin-adoption-price-theory2eed48ecf7da (2016). Accessed 25 Sep 2018
20. McComb, K.: [2018] Bitcoin Crash: Analysis of 8 Historical Crashes and What’s Next.
https://blog.purse.io/bitcoin-crash-e112ee42c0b5 (2018). Accessed 25 Sep 2018
21. Amadeo, K.: Stock Market Corrections Versus Crashes and How to Protect Yourself: How
You Can Tell If It’s a Correction or a Crash. https://www.thebalance.com/stock-marketcorrection-3305863 (2018). Accessed 25 Sep 2018
22. Donner, R.V., Small, M., Donges, J.F., Marwan, N., Zou, Y., Xiang, R., Kurths, J.:
Recurrence-based time series analysis by means of complex network methods. International
Journal of Bifurcation and Chaos. 21(04), 1019–1046 (2011).
doi:10.1142/S0218127411029021
23. Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuño, J.C.: From time series to complex
networks: The visibility graph. Proceedings of the National Academy of Sciences of the
United States of America. 105(13), 4972–4975 (2008). doi:10.1073/pnas.0709247105
24. Burnie, A.: Exploring the Interconnectedness of Cryptocurrencies using Correlation
Networks. arXiv:1806.06632 [q-fin.CP]. https://arxiv.org/pdf/1806.06632 (2018). Accessed
25 Sep 2018