Earticle

초록

영어

At the end of 1997, the volatility of the exchange rate intensified as the nation's exchange rate system was converted into a free-floating exchange rate system. As a result, managing the exchange rate is becoming a very important task, and the need for forecasting the exchange rate is growing. The exchange rate prediction model using the existing exchange rate prediction method, statistical technique, cannot find a nonlinear pattern of the time series variable, and it is difficult to analyze the time series with the variability cluster phenomenon. And as the number of variables to be analyzed increases, the number of parameters to be estimated increases, and it is not easy to interpret the meaning of the estimated coefficients. Accordingly, the exchange rate prediction model using artificial neural network, rather than statistical technique, is presented. Using DNN, which is the basis of deep learning among artificial neural networks, and LSTM, a recurrent neural network model, the number of hidden layers, neurons, and activation function changes of each model found the optimal exchange rate prediction model. The study found that although there were model differences, LSTM models performed better than DNN models and performed best when the activation function was Tanh.

목차

Abstract
1. Introduction
2. Related Research & Techniques
2.1 Related Research
2.2 Techniques
3. Proposed Exchange Rate Prediction Model
4. Results of the Experiment
5. Conclusion
References

키워드

저자

• Noo Ri Shin [ Graduate School of Smart Convergence Kwangwoon University, Korea ]
• Dai Yeol Yun [ Professor, Department of information and communication Engineering, Institute of Information Technology, Kwangwoon University, Seoul, 01897, Korea ] Corresponding Author
• Chi-gon Hwang [ Visiting Professor, Department of Computer Engineering, Institute of Information Technology, Kwangwoon University, Seoul, 01897, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380