EVALUATING THE PERFORMANCE OF BITCOIN PRICE FORECASTING USING MACHINE LEARNING TECHNIQUES ON HISTORICAL DATA
Mamun Ahmed
Bangladesh Army International University of Science & Technology, Computer Science & Engineering (Bangladesh)
https://orcid.org/0000-0002-3980-3981
Sayma Alam Suha
Bangladesh University of Professionals, Department of Computer Science & Engineering (Bangladesh)
https://orcid.org/0000-0002-7935-3698
Fahamida Hossain Mahi
Bangladesh Army International University of Science & Technology, Computer Science & Engineering (Bangladesh)
https://orcid.org/0009-0006-2624-3993
Forhad Uddin Ahmed
forhad.uddin@baiust.edu.bdBangladesh Army International University of Science & Technology, Computer Science & Engineering (Bangladesh)
https://orcid.org/0009-0008-1513-5238
Abstract
Since entering the market in 2009, Bitcoin has had a price that is extremely erratic. Its price is influenced by factors such as adoption rates, regulatory changes, geopolitical occurrences, and macroeconomic developments. Experts believe that Bitcoin's price will rise in the long run due to limited supply and rising demand. Therefore, the aim of this study is to propose an ensemble feature selection and machine learning-based approach to predict bitcoin price. For this research purpose, the cryptocurrency-based dataset has been used, visualized, and preprocessed. Five different feature selection approaches (Pearson, RFE, Embedded Random Forest, Tree-based and Light GBM) are followed by ensemble methodology, with the maximum voting approach to extract the most significant features and generate a dataset with reduced attributes. Then the dataset with or without feature selection is used for bitcoin price prediction by applying ten different machine learning regressing models, which includes six traditional, four bagging and boosting ensemble techniques. The comparative result analysis through multiple performance parameters reveals that the decreased number of features improves the performance for each of the models and the ensemble models outperform other types of models. Therefore, Random Forest regression ensemble ML model can get the best prediction accuracy with 0.036018 RMSE, 0.029470 MAE and 0.934512 R2 employing the dataset with reduced features for estimating the value of bitcoin.
Keywords:
machine learning, bitcoin, regression models, ensemble feature selectionReferences
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Authors
Mamun AhmedBangladesh Army International University of Science & Technology, Computer Science & Engineering Bangladesh
https://orcid.org/0000-0002-3980-3981
Authors
Sayma Alam SuhaBangladesh University of Professionals, Department of Computer Science & Engineering Bangladesh
https://orcid.org/0000-0002-7935-3698
Authors
Fahamida Hossain MahiBangladesh Army International University of Science & Technology, Computer Science & Engineering Bangladesh
https://orcid.org/0009-0006-2624-3993
Authors
Forhad Uddin Ahmedforhad.uddin@baiust.edu.bd
Bangladesh Army International University of Science & Technology, Computer Science & Engineering Bangladesh
https://orcid.org/0009-0008-1513-5238
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