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SCOPUS 학술저널

A Study on Predicting Cryptocurrency Distribution Prices Using Machine Learning Techniques

Purpose: Blockchain technology suggests ways to solve the problems in the existing industry. Among them, Cryptocurrency system, which is an element of Blockchain technology, is a very important factor for operating Blockchain. While Blockchain cryptocur rency has attracted at tention, studies on cryptocurrency prices have been mainly conducted, however previous studies mainly conducted on Bitcoin prices. On the other hand, in the context of the creation and trading of various cryptocurrencies based on the Blockc hain system, lit tle research has been done on cryptocurrencies other than Bitcoin. Hence, this study attempts to find variables related to th the prices of Dash, Litecoin, and Monero cryptocurrencies using machine learning techniques. We also attempt to find differences in t he variables related to the prices for each cryptocurrencies and to examine machine learning techniques that can provide better performance. Research design, data, and methodology methodology: This study performed Dash, Litecoin, and Monero price prediction analysis of cryptocurrency using Blockchain information and machine learning techniques. We employed number of transactions in Blockchain, amount of generated cryptocurrency, transaction fees, number of activity accounts in Blockchain, Block creation difficulty, blo ck size, umber of created blocks as independent variables. This study tried to ensure the reliability of the analysis results through 10 10-fold cross validation. Blockchain information was hierarchically added for price prediction, and the analysis result wa s measured as RMSE and MAPE. ResultsResults: The analysis shows that the prices of Dash, Litecoin and Monero cryptocurrency are related to Blockchain information. Also, we found that different Blockchain information improves the analysis results for each cryptocu rrency. In addition, this study found that the neural network machine learning technique provides better analysis results tha than supportsupport-vector m

1. 서론

2. 이론적 배경

3. 연구 설계

4. 분석 결과

5. 시사점

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