BITCOIN PRICE PREDICTION WITH ARIMAX AND DEEP LEARNING MODELS
Abstract
Deep learning has emerged as a widely applied approach across various fields, with finance and forecasting being among its most prominent areas of use. Within this domain, different deep learning architectures have been developed to address specific prediction problems.
This study compares the performance of ARIMAX and several deep learning models—including LSTM, BILSTM, CNN-LSTM, GRU, and TFT—in forecasting Bitcoin prices. The dataset consists of daily values from January 2014 to January 2025. The dependent variable is the daily Bitcoin closing price ($), while the independent variables include oil price (USD/barrel), gold price (USD/ounce), platinum price ($/XPT), and the USD/TRY exchange rate.
All analyses were conducted in Python using Google Colab, with the Keras library employed for model implementation. Root Mean Square Error (RMSE) was selected as the evaluation metric for predictive accuracy.
The results indicate that the TFT model achieved the highest predictive performance, followed closely by the GRU model. LSTM, BILSTM, and ARIMAX models showed similar yet weaker performance, while the CNN-LSTM model produced the least accurate forecasts, with significantly higher RMSE values compared to the other models.
Keywords
References
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