Earticle

초록

영어

This study conducts a comparative analysis of various models for predicting Bitcoin prices. The models utilized in this research include the Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Random Forest. Their performance was evaluated using key metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). The findings reveal that the GRU model outperformed the other two models, achieving an R² value of 0.9301, which indicates its superior ability to explain the data. The LSTM model demonstrated relatively high explanatory power with an R² of 0.8252, yet it exhibited lower predictive accuracy compared to the GRU. On the other hand, the Random Forest model recorded the lowest MAE (1557.44) and RMSE (1766.95), reflecting strong short-term prediction capabilities. However, its R² of 0.6110 highlighted limitations in capturing the intricate temporal patterns of the dataset. The significance of this study lies in several aspects. First, it empirically demonstrates that the GRU model, with its more streamlined architecture, can achieve superior predictive performance compared to the LSTM and Rando Forest models. Additionally, while Random Forest showed promising results for short-term predictions, it underscored limitations in modeling the complexities of temporal data patterns. Future research should aim to incorporate broader datasets and integrate external economic variables to enhance predictive accuracy. Furthermore, the potential of ensemble approaches, combining diverse models, holds promise for further improving predictive performance in this domain.

목차

Abstract
1. 서론
2. 이론적 배경
2.1 비트코인 시장의 특징
2.2 Gated Recurrent Unit(GRU)
2.3 LSTM(Long Short-Term Memory)
2.4 Random Forest
3. 연구 방법론
4. 분석 결과
4.1 변수의 변화 추이 분석 결과
4.2 GRU 분석 결과
4.3 LSTM 분석 결과
4.4 Random Forest 분석 결과
5. 결론
5.1 연구의 의의 및 향후 연구 제언
References

키워드

저자

• 유기섭 [ Giseob Yu | Adjunct Professor, Business School, Hanyang University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005