Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price Prediction
The significant increase in cryptocurrency trading on digital blockchain platforms has led to a growing interest in employing machine learning techniques for the effective prediction of highly nonlinear and nonstationary data, becoming increasingly popular among both individual and institutional mar...
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| Format: | Article |
| Language: | English |
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University of Zagreb, Faculty of organization and informatics
2024-01-01
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| Series: | Journal of Information and Organizational Sciences |
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| Online Access: | https://hrcak.srce.hr/file/472070 |
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| author | Sara Salehi |
| author_facet | Sara Salehi |
| author_sort | Sara Salehi |
| collection | DOAJ |
| description | The significant increase in cryptocurrency trading on digital blockchain platforms has led to a growing interest in employing machine learning techniques for the effective prediction of highly nonlinear and nonstationary data, becoming increasingly popular among both individual and institutional market participants. The aim of this research is to deal with the challenging task of predicting the closing prices of two prominent cryptocurrencies, Binance Coin (BNB) and Ethereum (ETH), utilizing machine-learning techniques. This study evaluates the efficacy of various machine learning models in predicting cryptocurrency prices, with a particular focus on Support Vector Machines for Regression (SVR), least-squares Boosting (LSBoost), and Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System (ANFIS). These models are compared under various metrics. ANFIS models exhibited superior predictive performance on both training and testing datasets based on diverse performance metrics. Comparatively, SVR with a linear kernel demonstrated strong generalization capabilities, particularly on the testing set. LSBoost, while showing promise in training accuracy, indicated results with higher test errors. ANN models maintained a balance between training and testing. This comparison showed the models’ effectiveness, particularly the robustness of ANFIS in capturing the volatile cryptocurrency market trends. The experimental data suggest that certain of the above models can be utilized to predict the ETH and BNB closing price in real time with promising accuracy and experimentally proven profitability. |
| format | Article |
| id | doaj-art-aace0e7bbbb743f6a397c79af1c1ddd8 |
| institution | Kabale University |
| issn | 1846-3312 1846-9418 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Zagreb, Faculty of organization and informatics |
| record_format | Article |
| series | Journal of Information and Organizational Sciences |
| spelling | doaj-art-aace0e7bbbb743f6a397c79af1c1ddd82025-01-10T10:07:13ZengUniversity of Zagreb, Faculty of organization and informaticsJournal of Information and Organizational Sciences1846-33121846-94182024-01-0148234135210.31341/jios.48.2.7Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price PredictionSara Salehi0Department of Computer Engineering, Cyprus International University, Lefkosa, North CyprusThe significant increase in cryptocurrency trading on digital blockchain platforms has led to a growing interest in employing machine learning techniques for the effective prediction of highly nonlinear and nonstationary data, becoming increasingly popular among both individual and institutional market participants. The aim of this research is to deal with the challenging task of predicting the closing prices of two prominent cryptocurrencies, Binance Coin (BNB) and Ethereum (ETH), utilizing machine-learning techniques. This study evaluates the efficacy of various machine learning models in predicting cryptocurrency prices, with a particular focus on Support Vector Machines for Regression (SVR), least-squares Boosting (LSBoost), and Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System (ANFIS). These models are compared under various metrics. ANFIS models exhibited superior predictive performance on both training and testing datasets based on diverse performance metrics. Comparatively, SVR with a linear kernel demonstrated strong generalization capabilities, particularly on the testing set. LSBoost, while showing promise in training accuracy, indicated results with higher test errors. ANN models maintained a balance between training and testing. This comparison showed the models’ effectiveness, particularly the robustness of ANFIS in capturing the volatile cryptocurrency market trends. The experimental data suggest that certain of the above models can be utilized to predict the ETH and BNB closing price in real time with promising accuracy and experimentally proven profitability.https://hrcak.srce.hr/file/472070Machine learningHybrid methodPredictionsCryptocurrency |
| spellingShingle | Sara Salehi Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price Prediction Journal of Information and Organizational Sciences Machine learning Hybrid method Predictions Cryptocurrency |
| title | Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price Prediction |
| title_full | Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price Prediction |
| title_fullStr | Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price Prediction |
| title_full_unstemmed | Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price Prediction |
| title_short | Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price Prediction |
| title_sort | comparative analysis of machine learning techniques for cryptocurrency price prediction |
| topic | Machine learning Hybrid method Predictions Cryptocurrency |
| url | https://hrcak.srce.hr/file/472070 |
| work_keys_str_mv | AT sarasalehi comparativeanalysisofmachinelearningtechniquesforcryptocurrencypriceprediction |