In today's digital age, financial data has emerged as a critical asset for investors, risk managers, and financial professionals alike. The intricate and volatile nature of financial markets presents substantial challenges in accurately forecasting financial time series. This study explores the application of the Transformer model, a pioneering advancement in deep learning, to enhance the precision of financial time series predictions. By leveraging the Transformer's robust capabilities in handling sequential data, this research aims to address the complexities inherent in financial forecasting, offering potential improvements in predictive accuracy and risk management strategies.
- Clone the repository:
git clone https://github.com/justinvo277/Forecasting-Financial-Time-Series.git
- Navigate to directory:
cd Forecasting-Financial-Time-Series - Install the required dependencies:
pip install -r requirements.txt
python train.py