Journal
SENSORS
Volume 22, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/s22051740
Keywords
exchange rate prediction; cryptocurrency; XGBoost; blockchain
Funding
- Ministry of Small and Medium-sized Enterprises (SMEs) and Startups (MSS), Korea, under the Regional Specialized Industry Development Program (RD) [S3091627]
- Ministry of SMEs and Startups (MSS), Korea, under the Startup growth technology development program (RD) [S3125114]
- Korea Technology & Information Promotion Agency for SMEs (TIPA) [S3125114, S3091627] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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The popularity of cryptocurrency has attracted attention in the academic field. In this study, the XGBoost algorithm and blockchain framework were used to predict the exchange rate, enhancing system security and transparency.
The popularity of cryptocurrency in recent years has gained a lot of attention among researchers and in academic working areas. The uncontrollable and untraceable nature of cryptocurrency offers a lot of attractions to the people in this domain. The nature of the financial market is non-linear and disordered, which makes the prediction of exchange rates a challenging and difficult task. Predicting the price of cryptocurrency is based on the previous price inflations in research. Various machine learning algorithms have been applied to predict the digital coins' exchange rate, but in this study, we present the exchange rate of cryptocurrency based on applying the machine learning XGBoost algorithm and blockchain framework for the security and transparency of the proposed system. In this system, data mining techniques are applied for qualified data analysis. The applied machine learning algorithm is XGBoost, which performs the highest prediction output, after accuracy measurement performance. The prediction process is designed by using various filters and coefficient weights. The cross-validation method was applied for the phase of training to improve the performance of the system.
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