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
Andi H. K.: An Accurate Bitcoin Price Prediction Using Logistic Regression with LSTM Machine Learning Model. Journal of Soft Computing Paradigm 3(3), 2021, 205–217 [https://doi.org/10.36548/jscp.2021.3.006].
Google Scholar
Arumalla G. S. et al.: Bitcoin price fluctuation analysis and prediction using machine learning. International Journal of Progressive Research in Engineering Management and Science – IJPREMS 03(03), 2022, 421–425.
Google Scholar
Auti A. et al.: Bitcoin Price Prediction Using Svm. International Journal of Engineering Applied Sciences and Technology 6(11), 2022, 226–229.
Google Scholar
Bhatt S. et al.: Machine Learning based Cryptocurrency Price Prediction using Historical Data and Social Media Sentiment. Computer Science & Information Technology – CS & IT 13, 2023, 1–11 [https://doi.org/10.5121/csit.2023.131001].
Google Scholar
Bhattad S. et al.: Review of Machine Learning Techniques for Cryptocurrency Price Prediction. EasyChair 10190, 2023.
Google Scholar
Chen J.: Analysis of Bitcoin Price Prediction Using Machine Learning. Journal of Risk and Financial Management 16(1), 2023, 51 [https://doi.org/10.3390/jrfm16010051].
Google Scholar
Chen W. et al.: Machine Learning Model for Bitcoin Exchange Rate Prediction Using Economic and Technology Determinants. International Journal of Forecasting 37(1), 2021, 28–43 [https://doi.org/10.1016/j.ijforecast.2020.02.008].
Google Scholar
Chen Z. et al.: Bitcoin Price Prediction Using Machine Learning: An Approach to Sample Dimension Engineering. Journal of Computational and Applied Mathematics 365, 2020, 112395 [https://doi.org/10.1016/j.cam.2019.112395].
Google Scholar
Chowdhury R. et al.: An Approach to Predict and Forecast the Price of Constituents and Index of Cryptocurrency Using Machine Learning. Physica. A 551, 2020, 124569 [https://doi.org/10.1016/j.physa.2020.124569].
Google Scholar
Dimitriadou A., Gregoriou A.: Predicting Bitcoin Prices Using Machine Learning. Entropy 25(5), 2023, 777 [https://doi.org/10.3390/e25050777].
Google Scholar
Erfanian S. et al.: Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach. Entropy 24(10), 2022, 1487 [https://doi.org/10.3390/e24101487].
Google Scholar
Gadey R. S. et al.: Price prediction of bitcoin using machine learning. International Journal of Engineering Applied Science and Technology 5(1), 2020, 502–506 [https://doi.org/10.33564/ijeast.2020.v05i01.089].
Google Scholar
Iqbal M. et al.: Time-Series Prediction of Cryptocurrency Market Using Machine Learning Techniques. EAI Endorsed Transactions on Creative Technologies 8(28), 2021, 170286 [https://doi.org/10.4108/eai.7-7-2021.170286].
Google Scholar
Islam M. R. et al.: Data-Driven Heart Disease Prediction by Ensemble Feature Selection and Machine Learning Techniques. 25th International Conference on Computer and Information Technology (ICCIT), 2022, 575–580 [https://doi.org/10.1109/iccit57492.2022.10054998].
Google Scholar
Jaquart P. et al.: Short-term Bitcoin Market Prediction via Machine Learning. Journal of Finance and Data Science 7, 2021, 45–66 [https://doi.org/10.1016/j.jfds.2021.03.001].
Google Scholar
Kavitha H. et al.: Performance Evaluation of Machine Learning Algorithms for Bitcoin Price Prediction. 2020 Fourth International Conference on Inventive Systems and Control (ICISC), 2020, [https://doi.org/10.1109/icisc47916.2020.9171147.
Google Scholar
Kervanci, I. S., Akay F.: Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods. Sakarya University Journal of Computer and Information Sciences 3(3), 2020, 272–282 [https://doi.org/10.35377/saucis.03.03.774276].
Google Scholar
Khedr A. M. et al.: Cryptocurrency Price Prediction Using Traditional Statistical and Machine‐learning Techniques: A Survey. International Journal of Intelligent Systems in Accounting, Finance & Management 28(1), 2021, 3–34 [https://doi.org/10.1002/isaf.1488].
Google Scholar
Kiranashree B. K. et al.: Price Prediction of Bitcoins. 22 Mar. 2023, [https://journal.ijmdes.com/ijmdes/article/view/115].
Google Scholar
Li Q.: Predicting Trends of Bitcoin Prices Based on Machine Learning Methods. 4th International Conference on Software and e-Business, 2020, 49–52 [https://doi.org/10.1145/3446569.3446588].
Google Scholar
Loh E. C.: Emerging Trend of Transaction and Investment: Bitcoin Price Prediction Using Machine Learning. International Journal of Advanced Trends in Computer Science and Engineering 9(1.4), 2020, 100–104 [https://doi.org/10.30534/ijatcse/2020/1591.42020].
Google Scholar
Mangla N. et al.: Bitcoin price prediction using machine learning. International Journal of Information and Computing Science 6(5), 2019, 318–320.
Google Scholar
Mujlid H.: A Survey on Machine Learning Approaches in Cryptocurrency: Challenges and Opportunities. 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), IEEE, 2023.
Google Scholar
Nagamani P. et al.: Bitcoin Price Prediction Using Machine Learning Algorithms. Advances in engineering research/Advances in Engineering Research, 2023, 389–396 [https://doi.org/10.2991/978-94-6463-252-1_43].
Google Scholar
Pabuçcu H. et al.: Forecasting the Movements of Bitcoin Prices: An Application of Machine Learning Algorithms. Quantitative Finance and Economics 4(4), 2020, 679–692 [https://doi.org/10.3934/qfe.2020031].
Google Scholar
Parvez S. J. et al.: Bitcoin price prediction using Random Forest Regression. Journal of Positive School Psychology, 2022, 4352–4358.
Google Scholar
Poongodi M. et al.: Bitcoin Price Prediction Using ARIMA Model. International Journal of Internet Technology and Secured Transactions 10(4), 2020, 396 [https://doi.org/10.1504/ijitst.2020.108130].
Google Scholar
Pour E. S. et al.: Cryptocurrency Price Prediction with Neural Networks of LSTM and Bayesian Optimization. European Journal of Business and Management Research 7(2), 2022, 20–27 [https://doi.org/10.24018/ejbmr.2022.7.2.1307].
Google Scholar
Pragadareddy K. T. et al.: Price prediction model of bitcoin using decision tree classification. International Journal of Food and Nutritional Sciences (IJFANS) 11(1), 2022.
Google Scholar
Ranjan S. et al.: Bitcoin Price Prediction: A Machine Learning Sample Dimension Approach. Computational Economics 61(4), 2022, 1617–1636 [https://doi.org/10.1007/s10614-022-10262-6].
Google Scholar
Reddy K. R. et al.: Bitcoin Price Prediction and Forecasting. International Research Journal of Engineering and Technology (IRJET) 9(04), 2022, 2395–0056.
Google Scholar
Ren Y.-S. et al.: Past, Present, and Future of the Application of Machine Learning in Cryptocurrency Research. Research in International Business and Finance 63, 2022, 101799 [https://doi.org/10.1016/j.ribaf.2022.101799].
Google Scholar
Roh Y. et al.: A Survey on Data Collection for Machine Learning: A Big Data – AI Integration Perspective. IEEE Transactions on Knowledge and Data Engineering 33(4), 2021, 1328–1347 [https://doi.org/10.1109/tkde.2019.2946162].
Google Scholar
Sahi G. et al.: Predicting Cryptocurrency Price Using Machine Learning. European Economic Letters (EEL) 13(1), 2023, 11–16.
Google Scholar
Samaddar M. et al.: A Comparative Study of Different Machine Learning Algorithms on Bitcoin Value Prediction. International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2021 [https://doi.org/10.1109/icaect49130.2021.9392629].
Google Scholar
Senthilkumar S., Nivedha B.: Bitcoin Price Prediction Using Ml. Social Science Research Network, 2022 [https://doi.org/10.2139/ssrn.4128261].
Google Scholar
Shahbazi Z., Byun Y.-C.: Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using Machine Learning Pipelines. Sensors 22(5), 2022, 1740 [https://doi.org/10.3390/s22051740].
Google Scholar
Shakri I. H.: Time Series Prediction Using Machine Learning: A Case of Bitcoin Returns. Studies in Economics and Finance 39(3), 2021, 458–470 [https://doi.org/10.1108/sef-06-2021-0217].
Google Scholar
Shankhdhar A. et al.: Bitcoin Price Alert and Prediction System Using Various Models. IOP Conference Series. Materials Science and Engineering 1131(1), 2021, 012009 [https://doi.org/10.1088/1757-899x/1131/1/012009].
Google Scholar
Squarepants S.: Bitcoin: A Peer-to-Peer Electronic Cash System. Social Science Research Network, 2008 [https://doi.org/10.2139/ssrn.3977007].
Google Scholar
Suha S. A., Sanam T. F.: A Machine Learning Approach for Predicting Patient’s Length of Hospital Stay With Random Forest Regression. IEEE Region 10 Symposium (TENSYMP), 2022 [https://doi.org/10.1109/tensymp54529.2022.9864447].
Google Scholar
Yan K., Wang Y.: Prediction of Bitcoin prices’ trends with ensemble learning models. Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2023, 900–905 [https://doi.org/10.1117/12.2667793].
Google Scholar
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
Statistics
Abstract views: 246PDF downloads: 161
Most read articles by the same author(s)
- Mamun Ahmed, Salma Binta Islam, Aftab Uddin Alif, Mirajul Islam, Sabrina Motin Saima, AI EMPOWERED DIAGNOSIS OF PEMPHIGUS: A MACHINE LEARNING APPROACH FOR AUTOMATED SKIN LESION DETECTION , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 13 No. 4 (2023)
