암호화폐 가격 예측을 위한 딥러닝 앙상블 모델링

• 영문명: Development of Deep Learning Ensemble Modeling for Cryptocurrency Price Prediction : Deep 4-LSTM Ensemble Model
• 발행기관: 한국IT서비스학회
• 저자명: 최수빈(Soo-bin Choi) 신동훈(Dong-hoon Shin) 윤상혁(Sang-Hyeak Yoon) 김희웅(Hee-Woong Kim)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제19권 제6호, 131~144쪽, 전체 14쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2020.12.30

영문 초록

As the blockchain technology attracts attention, interest in cryptocurrency that is received as a reward is also increasing. Currently, investments and transactions are continuing with the expectation and increasing value of cryptocurrency. Accordingly, prediction for cryptocurrency price has been attempted through artificial intelligence technology and social sentiment analysis. The purpose of this paper is to develop a deep learning ensemble model for predicting the price fluctuations and one-day lag price of cryptocurrency based on the design science research method. This paper intends to perform predictive modeling on Ethereum among cryptocurrencies to make predictions more efficiently and accurately than existing models. Therefore, it collects data for five years related to Ethereum price and performs pre-processing through customized functions. In the model development stage, four LSTM models, which are efficient for time series data processing, are utilized to build an ensemble model with the optimal combination of hyperparameters found in the experimental process. Then, based on the performance evaluation scale, the superiority of the model is evaluated through comparison with other deep learning models. The results of this paper have a practical contribution that can be used as a model that shows high performance and predictive rate for cryptocurrency price prediction and price fluctuations. Besides, it shows academic contribution in that it improves the quality of research by following scientific design research procedures that solve scientific problems and create and evaluate new and innovative products in the field of information systems

목차

1. 서 론

2. 개념적 배경

3. 연구방법론

4. 모델링 결과 및 성능 평가

5. 토의 및 시사점

참고문헌

키워드

Deep Learning Ensemble Modeling Cryptocurrency Design Science Price Prediction