earticle

영어

With the advancement of deep learning technology, research in time series forecasting is thriving across various fields. In the financial sector, where time series data is complex and volatile, making accurate predictions challenging, the importance of such research is growing as more people invest in financial markets. While deep learning models such as Autoencoders, Recurrent Neural Networks, Long Short-Term Memory networks, and Gated Recurrent Units are actively used in financial forecasting, the Transformer model, known for its efficiency and ability to address long-term dependency issues, has predominantly been applied to stock prediction through market sentiment analysis based on textual information rather than technical price predictions. Moreover, while there is extensive research on stock forecasting, there is a notable lack of studies on Exchange Traded Funds. This study aims to bridge this gap by using Transformer models to forecast future on Exchange Traded Funds prices and performing a comparative analysis of Transformer models of various sizes.