IAD Index of Academic Documents
  • Home Page
  • About
    • About Izmir Academy Association
    • About IAD Index
    • IAD Team
    • IAD Logos and Links
    • Policies
    • Contact
  • Submit A Journal
  • Submit A Conference
  • Submit Paper/Book
    • Submit a Preprint
    • Submit a Book
  • Contact
  • Savunma Bilimleri Dergisi
  • Volume:20 Issue:2
  • Predicting Bitcoin Price: Comparative Analysis of Machine Learning and Deep Learning Models

Predicting Bitcoin Price: Comparative Analysis of Machine Learning and Deep Learning Models

Authors : Ahmed İhsan Şimşek
Pages : 327-342
Doi:10.17134/khosbd.1394501
View : 54 | Download : 70
Publication Date : 2024-11-01
Article Type : Research Paper
Abstract :Bitcoin has become a prominent financial instrument in recent years, attracting increasing attention as a digital currency. Accurately forecasting the valuation of a financial asset carries substantial significance for both retail and institutional investors. The aim of this study is to evaluate and compare the predictive capabilities of various models, namely Support Vector Regression (SVR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a hybrid model combining CNN and Bidirectional LSTM (CNN-BiLSTM), and XGBoost, in the context of forecasting Bitcoin price. The main aim of this study is to ascertain the algorithm that demonstrates the most efficacy in forecasting the price of Bitcoin. This study utilizes the S&P500 index, Gold/Dollar exchange rate, West Texas Spot Oil Price, and Dollar Index as exogenous factors in order to forecast the price of Bitcoin. The dataset encompasses a consecutive time span of 2191 days, commencing on January 1, 2015 and concluding on September 18, 2023. The models outlined in the study undergo a two-stage procedure, including of training and testing. The assessment of the models\' performance was carried out by utilizing several statistical measures, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). The results indicate that the XGBoost algorithm had greater performance in projecting the price of Bitcoin, as evidenced by its consistently higher performance metrics across all evaluated aspects. The XGBoost model was succeeded by the CNN-BiLSTM, CNN, and LSTM models, which are hybrid methodologies, resulting in the most advantageous results. The SVR model demonstrated the least favorable performance..
Keywords : Zaman Serisi, Bitcoin, Derin Öğrenme, Makine Öğrenmesi, Fiyat Tahmini

ORIGINAL ARTICLE URL
VIEW PAPER (PDF)

* There may have been changes in the journal, article,conference, book, preprint etc. informations. Therefore, it would be appropriate to follow the information on the official page of the source. The information here is shared for informational purposes. IAD is not responsible for incorrect or missing information.


Index of Academic Documents
İzmir Academy Association
CopyRight © 2023-2025