Critical comparisons on deep learning approaches for foreign exchange rate prediction

ArXiv ID: 2307.06600 “View on arXiv”

Authors: Unknown

Abstract

In a natural market environment, the price prediction model needs to be updated in real time according to the data obtained by the system to ensure the accuracy of the prediction. In order to improve the user experience of the system, the price prediction function needs to use the fastest training model and the model prediction fitting effect of the best network as a predictive model. We conduct research on the fundamental theories of RNN, LSTM, and BP neural networks, analyse their respective characteristics, and discuss their advantages and disadvantages to provide a reference for the selection of price-prediction models.

Keywords: RNN, LSTM, BP neural networks, price prediction, real-time updating, General market prices (unspecified)

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Philosophers
• Why: The paper discusses neural network architectures with moderate equations but lacks detailed implementation code or backtesting results, focusing more on theoretical comparison than empirical validation.

  flowchart TD
    A["Research Goal: Compare<br>RNN, LSTM, BP for<br>FX Rate Prediction"] --> B["Input: Historical<br>Foreign Exchange Data"]
    B --> C["Computational Process:<br>Train & Evaluate 3 Models"]
    C --> D{"Comparative Analysis"}
    D --> E["RNN: Fast, but<br>Vanishing Gradient"]
    D --> F["LSTM: Best for Time Series<br>Long-term Dependencies"]
    D --> G["BP: Fast, but<br>Struggles with Sequential Data"]
    
    E --> H["Key Outcomes"]
    F --> H
    G --> H
    
    H["Key Outcomes: 1. LSTM best for<br>accuracy/fitting. 2. RNN/BP viable for<br>speed. 3. Need Real-time<br>Model Updating"]