단기 천연가스 가격 예측에 있어서 인공신경망의 활용에 대한 연구
An Application of Artificial Neural Network in Short-run Natural Gas Price Forecasting
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초록
인공신경망을 활용한 예측 모형은 우수한 예측력으로 인하여 시계열 분석에서도 꾸준하게 사용되어왔다. 이에 본 연구에서는 비전통가스의 본격적인 개발로 최근 많은 주목을 받고 있는 천연가스 가격을 단기적으로 예측하기 위한 인공신경망 모형을 구성해 보았다. 모형 구축을 위한 표본 자료로 Henry Hub 가격을 사용하였으며 최근 가장 활용도가 높은 역전파 알고리즘을 이용하였다. 고정식과 연속식 예측방식, 학습기간, 입력층과은닉층 노드를 조절한 모형을 설정하여 비교 분석하였다. 분석 결과, 연속 방식을 사용하고 입력 노드 6개, 은닉노드 10개로 설정한 모형이 예측 오차가 가장 작게 나타나 최적 모형인 것으로 나타났다.
Abstract
Because of its good performance in forecasting, artificial neural network(ANN) has been steadily appliedin time series analyses. We applied this ANN to forecast short-run natural gas prices which received a lot ofattention due to the boom of unconventional gas developments. The Henry Hub spot price was analyzed anda back-propagation algorithm which is the most popular method in ANN was applied. We changed ANNforecasting methods(cascading vs. fixed point), training periods, input numbers, and nodes of hidden layer fora comparison. As result, the cascading model with six inputs and ten nodes in the hidden layer had the minimumforecasting error which means the optimal model.
참고문헌 (21)
- Cooper, J. C. B., "Artificial neural networks versus multivariate statistics: An application from economics", Journal of Applied Statistics, 26(8), pp.909~921 (1999)
- Dutta, S., "Bond rating: a nonconservative application of neural networks", IEEE International Conference on Neural Networks, 442, pp.443~450 (1988)
- Etuk, E. H., "Seasonal Arima Modelling of Nigerian Monthly Crude Oil Prices", Asian Economic and Financial Review, 3(3), pp.333~340 (2013)
- Gutiérrez-Estrada, J. C., "Comparison between traditional methods and artificial neural networks for ammonia concentration forecasting in an eel (Anguilla anguilla L.)intensive rearing system", Aquacultural Engineering, 31(3-4), pp.183~203 (2004)
- Hartley, P. R., "The Future of Long-Term LNG Contracts, Vol. 1" (2013), Baker III Institute for Public Policy of Rice University
- 황광수, "국제 천연가스 가격과 원유 가격 사이의 탈동조화에 대한 연구", 한국자원공학회지, 49(3), pp.338~349 (2012), 한국자원공학회[DOI]
- Jeong, Y.G., "An investigation on the stock return predictability through artificial neural network", Korean journal of financial management, 15(2), pp.369~399 (1998)
- Kavousi-Fard, A., "A new hybrid correction method for short-term load forecasting based on ARIMA, SVR and CSA", Journal of Experimental &Theoretical Artificial Intelligence, 25(4), pp.559~574 (2013)
- Kim, D. S., "Neural networks theory and applications, Vol. 1" (2005), Jinhan M&B
- 이병철, "인공신경망을 이용한 비상장주식가치 평가방업의 도입가능성", 세무학연구, 22(2), pp.9~35 (2005), 한국세무학회
- 이형용, "한국 주가지수 등락 예측을 위한 유전자 알고리즘 기반 인공지능 예측기법 결합모형", Entrue Journal of Information Technology, 7(2), pp.33~43 (2008), 엘지씨엔에스
- Malik, F., "Forecasting output using oil prices: A cascaded artificial neural network approach", Journal of Economics and Business, 58(2), pp.168~180 (2006)
- Mazighi, A. E. H., "Henry Hub and national balancing point prices: what will be the international gas price reference?", OPEC review, 29(3), pp.219~230 (2005)
- Prybutok, V. R., "Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations", European Journal of Operational Research, 122(1), pp.31~40 (2000)
- Rosenblatt, F., "The perceptron: A probabilisitic model for information storage and organization in the brain", Psychoanalytic Review, 65(6), pp.386~408 (1958)
- Rumelhart, D. E., "Learning representations by backpropagating errors", Nature, 323(6088), pp.533~536 (1986)
- Rumelhart, D. E., "Parallel Distributed Processing: Explorations in the Microstructure of Cognition", pp.45~76 (1986), MIT Press
- Salchenberger, L. M., "Neural Networks: A New Tool for Predicting Thrift Failures", Decision Sciences, 23(4), pp.899~916 (1992)
- Wang, L., "An ARIMA-ANN Hybrid Model for Time Series Forecasting", Systems Research and Behavioral Science, 30(3), pp.244~259 (2013)
- Zhang, G., "Forecasting with artificial neural networks:: The state of the art", International Journal of Forecasting, 14(1), pp.35~62 (1998)
- Zhao, C.-l., "Fuzzy Information &Engineering and Operations Research & Management, Vol. 211", pp.275~286 (2014), Springer Berlin Heidelberg