| 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. |