Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field and CNNs for Stock Return Predictions

ArXiv ID: 2310.07427 “View on arXiv”

Authors: Unknown

Abstract

We propose a time series forecasting method named Quantum Gramian Angular Field (QGAF). This approach merges the advantages of quantum computing technology with deep learning, aiming to enhance the precision of time series classification and forecasting. We successfully transformed stock return time series data into two-dimensional images suitable for Convolutional Neural Network (CNN) training by designing specific quantum circuits. Distinct from the classical Gramian Angular Field (GAF) approach, QGAF’s uniqueness lies in eliminating the need for data normalization and inverse cosine calculations, simplifying the transformation process from time series data to two-dimensional images. To validate the effectiveness of this method, we conducted experiments on datasets from three major stock markets: the China A-share market, the Hong Kong stock market, and the US stock market. Experimental results revealed that compared to the classical GAF method, the QGAF approach significantly improved time series prediction accuracy, reducing prediction errors by an average of 25% for Mean Absolute Error (MAE) and 48% for Mean Squared Error (MSE). This research confirms the potential and promising prospects of integrating quantum computing with deep learning techniques in financial time series forecasting.

Keywords: Quantum Computing, Gramian Angular Field, Convolutional Neural Networks, Time Series Forecasting, Financial Data Transformation, Equities

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced quantum circuit design and deep learning (CNN) with specific mathematical transformations (QGAF), resulting in high mathematical complexity; it is backtest-ready with experiments on real financial data from three major markets and specific error metrics (MAE, MSE).

  flowchart TD
    A["Research Goal: Enhance Stock Return Prediction Accuracy"] --> B["Data: Stock Return Series<br/>China A-share, Hong Kong, US Markets"]
    B --> C["Methodology: Quantum Gramian Angular Field<br/>Circuit Design without Normalization/Inverse Cosine"]
    C --> D["Computational Process: Transform Time Series to 2D Images"]
    D --> E["Training: Convolutional Neural Networks CNNs"]
    E --> F["Forecasting: Time Series Classification & Prediction"]
    F --> G["Outcome: 25% Reduction in MAE & 48% Reduction in MSE<br/>vs. Classical GAF Method"]