Earticle

초록

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.

목차

Abstract
I. INTRODUCTION
II. EXPERIMENT
A. Dataset and Experimental Environment
B. Experimental Procedures
C. Result
III. CONCLUSION
REFERENCES

키워드

저자

• OH JOOHEE [ School of Computing Gachon University ]
• Sang-Woong Lee [ School of Computing Gachon University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004