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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.


Decomposed components.
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pone.0142064.g003: Decomposed components.

Mentions: Fig 3 shows the components generated by discrete wavelet decomposition. Clockwise from the upper left, we see the approximation component decomposed three times, whereas the detail component is shown decomposed three times, one time, and two times, respectively. This discrete wavelet decomposition can be carried out by the Daubechies, Coiflets, Symlets, or Discrete Meyer approach, among others. Among these wavelets, Daubechies and Symlets can be used for a perfect reconstruction with the maximum number of vanishing moments. Symlets are perfectly symmetrical; Daubechies are not. Because symmetry could decrease the flexibility in expressing data, we choose Daubechies wavelets. The number of vanishing moments of Daubechies wavelets was determined as three for the best MSE, within range of perfect reconstruction available.


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

Jin J, Kim J - PLoS ONE (2015)

Decomposed components.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0142064.g003: Decomposed components.
Mentions: Fig 3 shows the components generated by discrete wavelet decomposition. Clockwise from the upper left, we see the approximation component decomposed three times, whereas the detail component is shown decomposed three times, one time, and two times, respectively. This discrete wavelet decomposition can be carried out by the Daubechies, Coiflets, Symlets, or Discrete Meyer approach, among others. Among these wavelets, Daubechies and Symlets can be used for a perfect reconstruction with the maximum number of vanishing moments. Symlets are perfectly symmetrical; Daubechies are not. Because symmetry could decrease the flexibility in expressing data, we choose Daubechies wavelets. The number of vanishing moments of Daubechies wavelets was determined as three for the best MSE, within range of perfect reconstruction available.

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.