A Data-driven Deep Learning Approach for Bitcoin Price Forecasting

ArXiv ID: 2311.06280 “View on arXiv”

Authors: Unknown

Abstract

Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. Deep neural networks, on the other hand, has shown promising results recently; however, we require huge amount of high-quality data to leverage their power. There are some techniques such as augmentation that can help us with increasing the dataset size, but we cannot exploit them on historical bitcoin data. As a result, we propose a shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data using our proposed method to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network outperforms other popular price forecasting models.

Keywords: Bidirectional-LSTM (Bi-LSTM), feature engineering, deep neural networks, time-series forecasting, Cryptocurrency (Bitcoin)

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Philosophers
• Why: The paper employs standard deep learning architectures (Bi-LSTM) and statistical metrics (MAE, RMSE, MAPE) without advanced mathematical derivations, while the empirical approach relies on a limited, single-asset dataset with a custom train-test splitting strategy rather than rigorous backtesting across multiple assets or market regimes.

  flowchart TD
    A["Research Goal: Daily Bitcoin Closing Price Forecast"] --> B["Data Source: Historical Bitcoin Time-Series Data"]
    B --> C{"Proposed Methodology"}
    C --> D["Proposed Feature Engineering"]
    C --> E["Baseline Methods Comparison"]
    D --> F["Model Architecture: Shallow Bi-LSTM"]
    E --> G["ARIMA, LSTM, etc."]
    F --> H["Evaluation: RMSE, MAE"]
    G --> H
    H --> I["Outcome: Proposed FE + Shallow Bi-LSTM Outperforms Baselines"]