인공 신경망을 이용한 국제곡물가격예측 모형 개발
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 윤성주 | - |
dc.contributor.author | 이춘수 | - |
dc.contributor.author | 양승룡 | - |
dc.date.accessioned | 2021-09-04T07:54:42Z | - |
dc.date.available | 2021-09-04T07:54:42Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2016 | - |
dc.identifier.issn | 0549-6047 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/91131 | - |
dc.description.abstract | This suggest the prices forecast model for international grains using artificial neural network. For this, we first select the most suitable network models for grain prices and compare the predictive power with ARIMA (autoregressive and moving average model) and GARCH (generalized autoregressive conditional heteroskedasticity) models. The results show that the predictive power of artificial neural network is similar to or lower than the alternative models. When only past prices are considered, the neural network model has the higher predictive power compared to the cases with other market variables. Implications for forecasting of international grain prices are suggested at the end of this study. | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | 한국농업경제학회 | - |
dc.title | 인공 신경망을 이용한 국제곡물가격예측 모형 개발 | - |
dc.title.alternative | Development of Price Forecast Models for International Grains Using Artificial Neural Networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 양승룡 | - |
dc.identifier.bibliographicCitation | 농업경제연구, v.57, no.2, pp.83 - 101 | - |
dc.relation.isPartOf | 농업경제연구 | - |
dc.citation.title | 농업경제연구 | - |
dc.citation.volume | 57 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 83 | - |
dc.citation.endPage | 101 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002121975 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Artificial neural network | - |
dc.subject.keywordAuthor | International grain price | - |
dc.subject.keywordAuthor | Root mean square prediction error | - |
dc.subject.keywordAuthor | Price forecast | - |
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