Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김진수 | - |
dc.date.accessioned | 2017-09-08T01:20:52Z | - |
dc.date.available | 2017-09-08T01:20:52Z | - |
dc.date.issued | 2015-11 | - |
dc.identifier.citation | PLOS ONE, v. 10, NO 11, Page. 1-23 | en_US |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.uri | http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142064 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/28971 | - |
dc.description.abstract | Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency. | en_US |
dc.description.sponsorship | This work was supported by the Energy Efficiency & Resources Core Technology Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20152510101880). | en_US |
dc.language.iso | en | en_US |
dc.publisher | PUBLIC LIBRARY SCIENCE | en_US |
dc.subject | CRUDE-OIL PRICE | en_US |
dc.subject | ELECTRICITY MARKETS | en_US |
dc.subject | TRANSFORM | en_US |
dc.subject | MODELS | en_US |
dc.subject | ARIMA | en_US |
dc.subject | PREDICTION | en_US |
dc.subject | DECOMPOSITION | en_US |
dc.subject | PERFORMANCE | en_US |
dc.title | Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks | en_US |
dc.type | Article | en_US |
dc.relation.no | 11 | - |
dc.relation.volume | 10 | - |
dc.identifier.doi | 10.1371/journal.pone.0142064 | - |
dc.relation.page | 1-23 | - |
dc.relation.journal | PLOS ONE | - |
dc.contributor.googleauthor | Jin, Junghwan | - |
dc.contributor.googleauthor | Kim, Jinsoo | - |
dc.relation.code | 2015008685 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF EARTH RESOURCES AND ENVIRONMENTAL ENGINEERING | - |
dc.identifier.pid | jinsookim | - |
dc.identifier.researcherID | P-9559-2015 | - |
dc.identifier.orcid | http://orcid.org/0000-0001-6496-1714 | - |
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