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

TL;DR

This paper introduces Quantum Gramian Angular Field (QGAF), a novel quantum computing-based method for transforming stock return time series into images for CNN-based forecasting, outperforming classical methods in accuracy.

Contribution

The paper presents a new quantum-inspired transformation technique that simplifies data preprocessing and enhances forecasting accuracy in financial time series prediction.

Findings

QGAF reduces MAE by 25% compared to classical GAF.

QGAF reduces MSE by 48% compared to classical GAF.

QGAF improves prediction accuracy across multiple stock markets.

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.