Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using Machine Learning Pipelines
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 d...
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| Vydáno v: | Sensors (Basel, Switzerland) Ročník 22; číslo 5; s. 1740 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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MDPI AG
23.02.2022
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| ISSN: | 1424-8220, 1424-8220 |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Shahbazi, Zeinab Byun, Yung-Cheol |
| AuthorAffiliation | Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea; zeinab.sh@jejunu.ac.kr |
| AuthorAffiliation_xml | – name: Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea; zeinab.sh@jejunu.ac.kr |
| Author_xml | – sequence: 1 givenname: Zeinab orcidid: 0000-0003-1520-1799 surname: Shahbazi fullname: Shahbazi, Zeinab – sequence: 2 givenname: Yung-Cheol orcidid: 0000-0003-1107-9941 surname: Byun fullname: Byun, Yung-Cheol |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35270900$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Algorithms Artificial intelligence Blockchain Crypto-currencies cryptocurrency Data Mining Deep learning Digital currencies exchange rate prediction Feature selection Financial markets Foreign exchange rates Germany Humans Inflation (Finance) Information management Knowledge Discovery Literature reviews Machine Learning Market positioning Neural networks Peer to peer computing Volatility XGBoost |
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| Title | Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using Machine Learning Pipelines |
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