Financial Time Series Prediction Based on Deep Learning: Model Selection and Overfitting Control

Financial Time Series Prediction Based on Deep Learning: Model Selection and Overfitting Control

Problem Description
Financial time series prediction (e.g., stock prices, exchange rates, trading volume forecasting) is a core problem in fintech. Deep learning models (such as LSTM, Transformer) are widely used due to their ability to capture long-term dependencies and nonlinear patterns, but they face challenges such as overfitting and market non-stationarity. This topic requires an in-depth understanding of the basis for model selection, the causes of overfitting, and control methods.

Problem-Solving Process

  1. Problem Definition and Data Characteristic Analysis

    • Objective: Predict future financial indicators (e.g., returns) for a certain period.
    • Data Characteristics:
      • Non-stationarity (trends, periodicity, structural breaks);
      • High noise (interference from market sentiment, unexpected events);
      • Serial correlation (autocorrelation, heteroscedasticity).
    • Key Challenge: Avoid the model overfitting to historical noise and improve generalization capability.

  2. Basis for Model Selection

    • Recurrent Neural Network (LSTM):
      • Suitable scenarios: Medium- to short-term serial dependencies, such as intraday price fluctuations;
      • Advantages: Gating mechanisms mitigate gradient vanishing, memory cells retain long-term information;
      • Limitations: Lagged response to sudden events, relatively high computational cost.
    • Transformer Model:
      • Suitable scenarios: Long sequences, multi-factor correlations (e.g., macro data + market data);
      • Advantages: Self-attention mechanism dynamically weights important time points, high parallel computing efficiency;
      • Limitations: Requires large amounts of data, positional encoding may distort the strict temporal order of financial sequences.
    • Selection Principles:
      • Prioritize lightweight models (e.g., Temporal Convolutional Network - TCN) when data is limited;
      • To capture macro cycles, combine seasonal decomposition (e.g., STL decomposition) + residual prediction.

  3. Causes and Diagnosis of Overfitting

    • Causes:
      • Excessively high model complexity (too many layers/parameters);
      • Insufficient training data or excessive noise;
      • Defective feature engineering (e.g., using future information leading to data leakage).
    • Diagnostic Methods:
      • Training loss continues to decrease, while validation loss first decreases then increases (typical overfitting);
      • Significant discrepancy between backtesting results and training performance (e.g., training Sharpe ratio 2.0, backtest only 0.5).

  4. Strategies for Controlling Overfitting

    • Data Level:
      • Data augmentation: Generate synthetic data (e.g., via TimeGAN), ensuring the generated distribution aligns with the real market;
      • Rolling cross-validation: Divide training/validation sets in chronological order to avoid disrupting the temporal structure through random splitting.
    • Model Level:
      • Regularization techniques:
        • Dropout (e.g., randomly disconnecting connections between LSTM layers);
        • L2 Regularization (penalizing large weights to limit model complexity);
        • Early Stopping (monitoring validation loss and terminating training early).
      • Structural simplification: Reduce the number of network layers or hidden units, prioritizing a single-layer LSTM with an attention mechanism.
    • Ensemble Methods:
      • Sliding window ensemble: Take a weighted average of predictions from models across multiple time windows to reduce single-model volatility;
      • Time-series cross-validation ensemble: As illustrated, ensemble strategies for models from different time periods.

  5. Practical Considerations

    • Avoid using future information: For example, when standardizing, only use the mean/variance from the historical window;
    • Selection of evaluation metrics: Not only use MSE but also calculate financial indicators (e.g., Sharpe ratio, maximum drawdown);
    • Online learning: When market distribution shifts, regularly retrain the model using a sliding window.

Summary
Financial time series prediction requires balancing model complexity and generalization capability. Methods such as data augmentation, regularization, and cross-validation should be employed to suppress overfitting, while selecting appropriate models based on business scenarios (e.g., LSTM for short-term volatility, Transformer for multi-factor long-term correlations).