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

Jin J, Kim J - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Natural Resources and Environmental Engineering, Hanyang University, Seoul, Korea.

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.

No MeSH data available.


Flow chart of the study.
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pone.0142064.g001: Flow chart of the study.

Mentions: This paper employs wavelet decomposition and the ARIMA, GARCH, and ANN models, to forecast the Henry Hub weekly gas spot price. The importance of the boundary problem of wavelet decomposition is illuminated by applying two different decomposition approaches. In addition, the effect of detail components in forecasting is analyzed by comparing the forecasting results with or without the detail components. ARIMA and ANN are used to forecast approximation components, while ANN and GARCH are used to forecast detail components. A flowchart of this study is presented in Fig 1, and we designed our experiment in two ways to compare the boundary problem issue. The experiment 1 doesn’t consider the boundary problem, whereas the experiment 2 does. The detailed framework is described as follows:


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

Jin J, Kim J - PLoS ONE (2015)

Flow chart of the study.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4635006&req=5

pone.0142064.g001: Flow chart of the study.
Mentions: This paper employs wavelet decomposition and the ARIMA, GARCH, and ANN models, to forecast the Henry Hub weekly gas spot price. The importance of the boundary problem of wavelet decomposition is illuminated by applying two different decomposition approaches. In addition, the effect of detail components in forecasting is analyzed by comparing the forecasting results with or without the detail components. ARIMA and ANN are used to forecast approximation components, while ANN and GARCH are used to forecast detail components. A flowchart of this study is presented in Fig 1, and we designed our experiment in two ways to compare the boundary problem issue. The experiment 1 doesn’t consider the boundary problem, whereas the experiment 2 does. The detailed framework is described as follows:

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Natural Resources and Environmental Engineering, Hanyang University, Seoul, Korea.

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.

No MeSH data available.