Bitcoin_ML: An Efficient Framework for Bitcoin Price Prediction Using Machine Learning | ||
| Journal of Computing and Communication | ||
| Article 6, Volume 3, Issue 1, January 2024, Pages 70-87 PDF (1.87 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/jocc.2024.339923 | ||
| Authors | ||
| Maged Farouk1; Nashwa Shaker1; Diaa s AbdElminaam* 2; Omnia Elrashidy1; Lana Mandour1; Malak Mesbah1; Jana Walid1; Mariam Ahmed1; Rawan Attia1; Nouran Ahmed1; Reda Elazab1 | ||
| 1Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt | ||
| 2Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt | ||
| Abstract | ||
| Econometrics can be used to understand and forecast price movements, assess market efficiency, and explore the factors influencing Bitcoin's value and adaptation. Econometrics is related to bitcoin in seven categories: price analysis and prediction, market efficiency, determination of Bitcoin prices, risk analysis, adaptation and network effects, causality tests, and simulation and stress. Testing these analyses can be invaluable for policymakers, investors, and financial institutions interested in the economics of digital currencies. Bitcoin price prediction in machine learning has many challenges that have deep roots in 2 main properties: cryptocurrencies and complexities in the Machine Learning models. Many problems are associated with machine learning for bitcoin price prediction, such as overfitting, data quality and availability, latent variables, model interpretability, computational complexity, dynamic adaptation, market manipulation, anomalies, data snooping bias risk, and time horizon mismatch. In the paper, we proposed an efficient framework for the prediction of bitcoin using nine different machine learning algorithms (linear Regression, random forest, adaboost, tree, KNN, gradient boosting, constant, neural network, SVM) on five different datasets. The results revealed that linear Regression emerged as the optimal model for the first data set. In the second data set, the random forest model demonstrated superior performance. The third data set exhibited the highest efficacy when the Adaboost model was employed. The fourth data set yielded the best outcomes with the random forest model, while linear Regression was the most effective choice for the final data set. | ||
| Keywords | ||
| Bitcoin Price Prediction; Machine Learning; Artificial Intelligence; Algorithm; Linear Regression Random Forest | ||
| References | ||
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