IDEAS home Printed from https://ideas.repec.org/a/abq/ijist1/v6y2024i2p396-412.html

Bitcoin Price Forecasting: A Comparative Study of Machine Learning, Statistical and Deep Learning Models

Author

Listed:
  • Neelam Urooj

    (Institute of Business Management and Administrative Sciences(IBM & AS), The Islamia University of Bahawalpur,Bahawalpur, Pakistan)

Abstract

Introduction/Importance of Study:Cryptocurrency price prediction is crucial for investors and researchers, given the market's nonlinear nature and the potential for significant financial implications. Novelty:This study offers a novel approach to cryptocurrency price prediction, leveraging a range of machine learning and deep learning models to address the challenges of predicting Bitcoin's exchange rate. Materials & Methods:The study employs various machine learning and deep learning models, including Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), along with traditional models like Linear Regressor, Random Regressor, ExtraTreesClassifier, XGBoost Regressor, ARIMA, Prophet, and CNN. Results & Discussion:The ExtraTreesClassifier model emerged as the top performer, achieving a Test MAPE of 0.0689. This model outperformed deep learning models like RNNs, indicating its effectiveness in cryptocurrency price prediction. Conclusion:The findings suggest that the proposed models, particularly the ExtraTreesClassifier, can provide valuable insights for investors and traders in the cryptocurrency market.

Suggested Citation

  • Neelam Urooj, 2024. "Bitcoin Price Forecasting: A Comparative Study of Machine Learning, Statistical and Deep Learning Models," International Journal of Innovations in Science & Technology, 50sea, vol. 6(2), pages 396-412, April.
    • Handle: RePEc:abq:ijist1:v:6:y:2024:i:2:p:396-412
    as

    Download full text from publisher

    File URL: https://journal.50sea.com/index.php/IJIST/article/view/732/1329
    Download Restriction: no
    File URL: https://journal.50sea.com/index.php/IJIST/article/view/732
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yinghao Chen & Xiaoliang Xie & Tianle Zhang & Jiaxian Bai & Muzhou Hou, 2020. "A deep residual compensation extreme learning machine and applications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 986-999, September.
    2. Jia‐Yen Huang & Jin‐Hao Liu, 2020. "Using social media mining technology to improve stock price forecast accuracy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 104-116, January.
    3. Manish Kumar & M. Thenmozhi, 2014. "Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 5(3), pages 284-308.
    4. Dirk G. Baur & Thomas Dimpfl, 2021. "The volatility of Bitcoin and its role as a medium of exchange and a store of value," Empirical Economics, Springer, vol. 61(5), pages 2663-2683, November.
    5. Andr'es Garc'ia-Medina & Toan Luu Duc Huynh3, 2021. "What drives bitcoin? An approach from continuous local transfer entropy and deep learning classification models," Papers 2109.01214, arXiv.org.
    6. Erdinc Akyildirim & Ahmet Goncu & Ahmet Sensoy, 2021. "Prediction of cryptocurrency returns using machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 3-36, February.
    7. Min Xu & Xingtong Chen & Gang Kou, 2019. "A systematic review of blockchain," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-14, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    2. Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
    3. Levantesi, Susanna & Piscopo, Gabriella & Roviello, Alba, 2025. "Cryptocurrency in global dynamics: Analyzing the Crypto Volatility Index and financial markets with machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
    4. Yu Song & Bo Chen & Xin-Yi Wang, 2023. "Cryptocurrency technology revolution: are Bitcoin prices and terrorist attacks related?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-20, December.
    5. Ahmed BenSaïda, 2023. "The linkage between Bitcoin and foreign exchanges in developed and emerging markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-27, December.
    6. Yuk Ming Tang & Ka Yin Chau, 2025. "Blockchain evolution and management theoretical (BEMT) model based on integrated semantic similarity and co-citation analysis for next generation service industry," Operations Management Research, Springer, vol. 18(2), pages 475-494, June.
    7. Aktham Maghyereh & Salem Adel Ziadat, 2024. "Pattern and determinants of tail-risk transmission between cryptocurrency markets: new evidence from recent crisis episodes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-34, December.
    8. Lee, Carmen Kar Hang & Leung, Eric Ka Ho, 2023. "Spatiotemporal analysis of bike-share demand using DTW-based clustering and predictive analytics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    9. Dante Miller & Jong-Min Kim, 2021. "Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies," JRFM, MDPI, vol. 14(10), pages 1-10, October.
    10. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
    11. Boyu Liu & Xiameng Si & Haiyan Kang, 2022. "A Literature Review of Blockchain-Based Applications in Supply Chain," Sustainability, MDPI, vol. 14(22), pages 1-24, November.
    12. Andrea Caliciotti & Marco Corazza & Giovanni Fasano, 2024. "From regression models to machine learning approaches for long term Bitcoin price forecast," Annals of Operations Research, Springer, vol. 336(1), pages 359-381, May.
    13. Roy Cerqueti & Mario Maggi & Jessica Riccioni, 2024. "Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk," Annals of Operations Research, Springer, vol. 342(3), pages 1445-1469, November.
    14. Rodrigo Hakim das Neves, 2020. "Bitcoin pricing: impact of attractiveness variables," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-18, December.
    15. ENNADIFI Imane & KADIL Ghizlane, 2025. "Exploring Tail Risk Transmission between Volatility Indices and Cryptocurrencies: Evidence from Quantile Connectedness," Advances in Decision Sciences, Asia University, Taiwan, vol. 29(3), pages 119-157, September.
    16. Ritu Singhal & Varun Sharma & Ishita Singhal & Vikas Bansal, 2024. "Blockchain-enabled auction for cloud resource provisioning: a survey on trust and economy," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(7), pages 2787-2807, July.
    17. Jaydip Sen & Sidra Mehtab, 2021. "Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models," Papers 2103.15096, arXiv.org.
    18. Muneer M. Alshater & Mayank Joshipura & Rim El Khoury & Nohade Nasrallah, 2023. "Initial Coin Offerings: a Hybrid Empirical Review," Small Business Economics, Springer, vol. 61(3), pages 891-908, October.
    19. Elie Bouri & Mahdi Ghaemi Asl & Sahar Darehshiri & David Gabauer, 2024. "Asymmetric connectedness between conventional and Islamic cryptocurrencies: Evidence from good and bad volatility spillovers," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-26, December.
    20. Juan F. Prados-Castillo & Miguel Ángel Solano-Sánchez & Pilar Guaita Fernández & José Manuel Guaita Martínez, 2023. "Potential of the Crypto Economy in Financial Management and Fundraising for Tourism," Sustainability, MDPI, vol. 15(6), pages 1-15, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:abq:ijist1:v:6:y:2024:i:2:p:396-412. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Iqra Nazeer (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.