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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2018.5
- 수록면
- 65 - 71 (7page)
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초록· 키워드
오류제보하기
As bitcoin attracts public attention as a new method of investment, the bitcoin market becomes a field of research that should be technically analyzed. Since bitcoin market is very uncertain, time series associated with bitcoin prices are complex, nonstationary and chaotic. In this paper, we attempt to predict bitcoin market trend using deep neural network (DNN) and four types of recurrent neural networks (RNNs). Our data set consists of fourteen input variables related to the bitcoin prices recorded daily from September 1, 2013 to August 31, 2017. Thirteen input variables are related to bitcoin prices, which are date, market price, high price, low price and closing price, and eight technical indicators derived from bitcoin prices. The other is keyword feature value obtained through text mining for social network services (SNS) data. Empirical study shows that DNN outperforms four RNNs in terms of specificity, precision and accuracy. Bidirectional RNN outperforms the other four deep learning models in terms of sensitivity. As a whole, DNN works quite well compared with RNNs.
목차
Abstract
1. Introduction
2. Deep Learning Models
3. Technical Indicators
4. Keyword Feature Value for SNS Data
5. Experimental Results
6. Conclusion
References
참고문헌 (10)
참고문헌 신청
[학술지(정기간행물)] - 강수지 / 2017 / Deep Recommender System Using Purchase History Data / Quantitative Bio-Science 36 (1) : 45 ~ 50
[학술대회논문] - Kingma DP / 2015 / Adam: A method for stochastic optimization / International Conference on Learning Representations
[학술대회논문] - Glorot X / 2010 / Understanding the difficulty of training deep feedforward neural networks / International Conference on Artificial Intelligence and Statistics : 249 ~ 256
[학술지(정기간행물)] - Hinton GE / 2006 / A fast learning algorithm for deep belief nets / Neural Comput 18 : 1527 ~ 1554
[학술지(정기간행물)] - Elman J / 1990 / Finding structure in time / Cognitive Science 14 : 179 ~ 211
[학술대회논문] - Kingma DP / 2015 / Adam: A method for stochastic optimization / International Conference on Learning Representations
[학술대회논문] - Glorot X / 2010 / Understanding the difficulty of training deep feedforward neural networks / International Conference on Artificial Intelligence and Statistics : 249 ~ 256
[학술지(정기간행물)] - Hinton GE / 2006 / A fast learning algorithm for deep belief nets / Neural Comput 18 : 1527 ~ 1554
[학술지(정기간행물)] - Elman J / 1990 / Finding structure in time / Cognitive Science 14 : 179 ~ 211
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UCI(KEPA) : I410-ECN-0101-2018-047-002155044