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Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks

Title
Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks
Author
김진수
Keywords
CRUDE-OIL PRICE; ELECTRICITY MARKETS; TRANSFORM; MODELS; ARIMA; PREDICTION; DECOMPOSITION; PERFORMANCE
Issue Date
2015-11
Publisher
PUBLIC LIBRARY SCIENCE
Citation
PLOS ONE, v. 10, NO 11, Page. 1-23
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.
URI
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142064http://hdl.handle.net/20.500.11754/28971
ISSN
1932-6203
DOI
10.1371/journal.pone.0142064
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > EARTH RESOURCES AND ENVIRONMENTAL ENGINEERING(자원환경공학과) > Articles
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