OCENA SKUTECZNOŚCI PROGNOZOWANIA CEN BITCOINÓW PRZY UŻYCIU TECHNIK UCZENIA MASZYNOWEGO NA DANYCH HISTORYCZNYCH

Mamun Ahmed


Bangladesh Army International University of Science & Technology, Computer Science & Engineering (Bangladesz)
https://orcid.org/0000-0002-3980-3981

Sayma Alam Suha


Bangladesh University of Professionals, Department of Computer Science & Engineering (Bangladesz)
https://orcid.org/0000-0002-7935-3698

Fahamida Hossain Mahi


Bangladesh Army International University of Science & Technology, Computer Science & Engineering (Bangladesz)
https://orcid.org/0009-0006-2624-3993

Forhad Uddin Ahmed

forhad.uddin@baiust.edu.bd
Bangladesh Army International University of Science & Technology, Computer Science & Engineering (Bangladesz)
https://orcid.org/0009-0008-1513-5238

Abstrakt

Od momentu wejścia na rynek w 2009 roku, cena Bitcoina jest niezwykle nieregularna. Na jego cenę wpływają takie czynniki, jak wskaźniki popularności, zmiany regulacyjne, wydarzenia geopolityczne i zmiany makroekonomiczne. Eksperci uważają, że cena Bitcoina wzrośnie w dłuższej perspektywie ze względu na ograniczoną podaż i rosnący popyt. Dlatego też celem niniejszego badania jest zaproponowanie podejścia opartego na selekcji cech i uczeniu maszynowym do przewidywania ceny bitcoina. Do tego celu badawczego wykorzystano, zwizualizowano i wstępnie przetworzono zbiór danych oparty na kryptowalutach. Zastosowano pięć różnych podejść do wyboru cech (Pearson, RFE, Embedded Random Forest, Tree-based i Light GBM), a następnie metodologię ensemble, z podejściem maksymalnego głosowania w celu wyodrębnienia najważniejszych cech i wygenerowania zbioru danych ze zredukowanymi atrybutami. Następnie zbiór danych z lub bez selekcji cech jest wykorzystywany do przewidywania cen bitcoinów poprzez zastosowanie dziesięciu różnych modeli regresji uczenia maszynowego, w tym sześciu tradycyjnych, czterech technik baggingu i boostingu. Analiza porównawcza wyników za pomocą wielu parametrów wydajności pokazuje, że zmniejszona liczba cech poprawia wydajność każdego z modeli, a modele zespołowe przewyższają inne typy modeli. W związku z tym model Random Forest regression ensemble ML może uzyskać najlepszą dokładność przewidywania z 0,036018 RMSE, 0,029470 MAE i 0,934512 R2, wykorzystując zbiór danych ze zredukowanymi funkcjami do szacowania wartości bitcoinów.


Słowa kluczowe:

uczenie maszynowe, bitcoin, modele regresji, selekcja cech zespołu

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


Opublikowane
2024-06-30

Cited By / Share

Ahmed, M., Suha, S. A., Hossain Mahi, F., & Uddin Ahmed, F. (2024). OCENA SKUTECZNOŚCI PROGNOZOWANIA CEN BITCOINÓW PRZY UŻYCIU TECHNIK UCZENIA MASZYNOWEGO NA DANYCH HISTORYCZNYCH. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(2), 101–108. https://doi.org/10.35784/iapgos.5657

Autorzy

Mamun Ahmed 

Bangladesh Army International University of Science & Technology, Computer Science & Engineering Bangladesz
https://orcid.org/0000-0002-3980-3981

Autorzy

Sayma Alam Suha 

Bangladesh University of Professionals, Department of Computer Science & Engineering Bangladesz
https://orcid.org/0000-0002-7935-3698

Autorzy

Fahamida Hossain Mahi 

Bangladesh Army International University of Science & Technology, Computer Science & Engineering Bangladesz
https://orcid.org/0009-0006-2624-3993

Autorzy

Forhad Uddin Ahmed 
forhad.uddin@baiust.edu.bd
Bangladesh Army International University of Science & Technology, Computer Science & Engineering Bangladesz
https://orcid.org/0009-0008-1513-5238

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