Bitcoin Piyasa Değerinin Analizinde Veri Madenciliği Teknikleriyle Bir Öncü Gösterge Yaklaşımı

Year 2021, Issue: 32, 20 - 26, 31.12.2021

Özerk Yavuz

https://doi.org/10.31590/ejosat.1039530

Abstract

Günümüzde son yıllarda iş ortamındaki değişikliklerin bir sonucu olarak yeni ödeme sistemleri gelişti. Bunun sonucunda tüketiciler, iş paydaşları, yatırımcılar ve bazı bireyler, çeşitli nedenlerle farklı ödeme sistemlerine ve sanal para birimlerine yöneldi. Bir blok zinciri mekanizması kullanan eşler arası mimarili Bitcoin, hayatımızda yer bulan bu yaklaşımlardan biridir. Bu çalışmada, Bitcoin piyasa değeri ve bitcoin değerlemesinin analizinde öncü gösterge odaklı bir veri madenciliği metodolojisi izlenmiştir. Literatür taraması, verilerin ön işlenmesi ve kavramsal çerçeve oluşturulmasının ardından verilere çeşitli sınıflandırma ve kümeleme algoritmaları uygulanmıştır. Son olarak, uygulanan bu denetimli ve denetimsiz makine öğrenmesi tekniklerinin keşfedilen kurallar ile performansları, bu tür problem ve araştırma alanları için karşılaştırılmış, değerlendirilmiş ve paylaşılmıştır.

Keywords

Dijital Para Birimi, Ödeme Sistemleri, Bitcoin, Blockchain, Kümeleme, Sınıflandırma, Denetimli Öğrenme, Denetimsiz Öğrenme, Veri Madenciliği, Makine Öğrenimi, Kantitatif Analiz

A Leading Indicator Approach with Data Mining Techniques in Analysing Bitcoin Market Value

Abstract

In the last decade as a result of the changes in business landscape new payment systems have evolved. Some of the Consumers, business stakeholders, investors and individuals turned to different types of payment systems and virtual currencies for various reasons. Peer to peer architectured Bitcoin which uses a blockchain mechanism is one of these approaches that found place in our lives. In this study, a leading indicator focused data mining methodology has been followed in analyzing Bitcoin market value and bitcoin valuation. Several classification and clustering algorithms applied to the data following a literature review, pre-processing of the data and conceptual framework formation. Finaly performances of these supervised and unsupervised machine learning techniques with rules discovered have been compared, assessed and presented for this type of problem and research domains.

Keywords

Digital Currency, Payment Systems, Bitcoin, Blockchain, Clustering, Classification, Supervised Learning, Unsupervised Learning, Data Mining, Machine Learning, Quantitative Analysis

References

• "Statement of Jennifer Shasky Calvery, Director Financial Crimes Enforcement Network United States Department of the Treasury Before the United States Senate Committee on Banking, Housing, and Urban Affairs Subcommittee on National Security and International Trade and Finance Subcommittee on Economic Policy" (PDF). fincen.gov. Financial Crimes Enforcement Network. 19 November 2013. Archived (PDF) from the original on 9 October 2016. Retrieved 1 June 2014.
• Böhme, Rainer, Nicolas Christin, Benjamin Edelman, and Tyler Moore. (2015). "Bitcoin: Economics, Technology, and Governance." Journal of Economic Perspectives, 29 (2): 213-38.
• SWOT Analysis: Discover New Opportunities, Manage and Eliminate Threats". www.mindtools.com. 2016. Retrieved 24 February 2018.
• Sammut-Bonnici, Tanya & Galea, David. (2015). SWOT Analysis. 10.1002/9781118785317.weom120103.
• Satoshi Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System,2008
• Águila, R.D.M., Ramírez, G.A., (2013). Series: basic statistics for busy clinicians. Allergol Immunopathol. 42 (5), pp. 485-492.
• Blackmore, K., Bossomaier, T., (2002). Comparison of See5 and J48.PART algorithms for missing persons profiling. International Conference on Information Technology and Applications
• Frank E. and Witten I.H. (1998). Generating Accurate Rule Sets Without Global Optimization. In Shavlik, J., ed., Machine Learning: Proceedings of the Fifteenth International Conference, Morgan Kaufmann Publishers.
• Frank E. and Witten I.H. (2000). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers: San Francisco, CA.
• Lemeshow S., Hosmer D.W., Klar J. & Lwanga S.K., 1990. Adequacy of sample size in health studies. Chichester: John Wiley and Sons.
• Merriam-Webster, (2020). https://www. merriam-webster.com [date accessed 9 August 2020]
• Ramchoun, H. r., Idrissi, M. m., Ghanou, Y. y., & Ettaouil, M. m. (2017). New Modeling of Multilayer Perceptron Architecture Optimization with Regularization: An Application to Pattern Classification. IAENG International Journal of Computer Science, 44(3), 261-269.
• Rosenblatt, F., & Cornell Aeronautical Laboratory. (1958). The perceptron: A theory of statistical separability in cognitive systems (Project Para). Buffalo, N.Y: Cornell Aeronautical Laboratory.
• Shearer, C., (2000) The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing, 5, 13-22.
• Simoudis, E. (1996). Reality Check for Data Mining. IEEE EXPERT, 11(5), pp.26-33
• Cohen, W. (1995). Fast effective rule induction. In A. Prieditis and S. Russell (eds.), Proceedings of the 12th International Conference on Machine Learning, Lake Tahoe, CA, pp.115-123.
• Saravanan, N., Gayathri V., (2018). Performance and classification evaluation of J48 algorithm and Kendall's based J48 algorithm (KNJ48). International Journal of Computer Trends and Technology
• Sasaki M., Kita K., (1998). Rule based text categorization using hierarchical categories, IEEE
• Surveymonkey, (2017). https://www.surveymonkey.com/mp/sample-size-calculator/ [date accessed 28 October 2017]
• Taniguchi M., Haft M., Hollm´en J., and Tresp V. (1998). Fraud detection in communications networks using neural and probabilistic methods. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'98), Volume II, pp. 1241-1244.
• Venkatesan, E. V., (2015). Performance Analysis of Decision Tree Algorithms for Breast Cancer Classification. Indian Journal of Science and Technology.
• Yavuz Ö., (2019), A data mining approach for desire and intention to participate in virtual communities. International Journal of Electrical and Computer Engineering, 9(5).
• Ławrynowicz, A., Tresp, V., (2014). Introducing Machine Learning. Perspectives on Ontology Learning. AKA Heidelberg /IOS Press.
• Thomas, M., (2012). Root Mean Square Error Compared to, and Contrasted with, Standard Deviation. Surveying and Land Information Science, 72.
• Ławrynowicz, A., Tresp, V., (2014). Introducing Machine Learning. Perspectives on Ontology Learning. AKA Heidelberg /IOS Press.
• Thomas, M., (2012). Root Mean Square Error Compared to, and Contrasted with, Standard Deviation. Surveying and Land Information Science, 72.
• Karahoca D., Karahoca A., Yavuz Ö., (2013). An early warning system approach for the identification of currency crises with data mining techniques. Neural Computing and Applications, 23(7-8)
• Rasmussen, C. E.; Williams, C. K. I. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning); The MIT Press: 2005.
• http://old.opentox.org/dev/documentation/components/gaussianregressions
• Anil Rajput, (2011) J48 and JRIP Rules for E-Governance Data, International Journal of Computer Science and Security (IJCSS), 5(2)
• Dr. E. O. Aruma, (2017), Abraham Maslow’s Hıerarchy Of Needs And Assessment Of Needs In Communıty Development, 5(7), International Journal of Development and Economic Sustainability
• Kaminsky GL, Reinhart CM (1996) The twin crises: the causes of banking and balance-of-payments problems. Board of Governors Federal Reserve System, Washington, DC 4.
• Kaminsky G, Lizondo S, Reinhart CM (1998) Leading indicators of currency crisis. Staff Pap Int Monet Fund 45(1):1–48 5. Kaminsky GL,
• Reinhart CM (1999) The twin crises: the causes of banking and balance-of-payments problems. Am Econ Rev 89(3):473–500
• Graham, B., & Dodd, D. L. (1951). Security analysis: Principles and technique. New York: McGraw-Hill.
• MLA. Kotler, Philip. Principles of Marketing. Englewood Cliffs, N.J. :Prentice Hall, 1991.
• MLA. Kotler, Philip. Marketing Management. Upper Saddle River, N.J. :Prentice Hall, 2000.