Bitcoin Piyasa Değerinin Analizinde Veri Madenciliği Teknikleriyle Bir Öncü Gösterge Yaklaşımı

Year 2021, , 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

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