Predictive Analysis of the Cryptocurrencies’ Movement Direction Using Machine Learning Methods

Küçük Resim Yok

Tarih

2021

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Springer Science and Business Media Deutschland GmbH

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Cryptocurrencies are among the most interesting financial instruments of recent years. Unlike the classical understanding that money exists as a means of change from one hand to another, this digital economy has begun to attract people's attention. The most popular currency emerging from this concept of cryptocurrency is “Bitcoin”. As the popularity of Bitcoin started to increase since 2016, the number of academic studies on cryptocurrencies has increased in parallel. In light of these developments, our study proposes predictive models of price change directions of high market value cryptocurrencies. Bitcoin, Ethereum and Litecoin were selected as cryptocurrencies and daily opening, closing, high and low prices of these currencies were collected from financial websites. Preprocessing was performed on the collected data to create input vectors. These vectors were given regression algorithms which are Multiple Linear, Polynomial, Support Vector, Decision Tree and Random Forest Regression. As evaluation metrics, R-square Method (R2) and Root Mean Square Error (RMSE) were chosen. After doing experiments with different parameter settings, it was found out that the chosen machine learning models showed satisfactory performances in predicting the directions of then mentioned cryptocurrencies. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Açıklama

Anahtar Kelimeler

Cryptocurrency, Data mining, Prediction, Regression

Kaynak

Lecture Notes on Data Engineering and Communications Technologies

WoS Q Değeri

Scopus Q Değeri

Q3

Cilt

76

Sayı

Künye