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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.


One-step forecasting results for wavelets combined with ANN and wavelets combined with ARIMA.
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pone.0142064.g008: One-step forecasting results for wavelets combined with ANN and wavelets combined with ARIMA.

Mentions: Figs 11 and 12 present the results for “adjusted forecasting.” Comparing them to Figs 8 and 10, we can see that the results no longer represent a smoothed version of the original data. When comparing Tables 5 and 6 to Tables 7 and 8, it becomes clear that there is a considerable overestimation of forecasting when the boundary condition is not considered. Although the forecasting performance decreases when the boundary condition is taken into account, based on comparing the results of Tables 7 and 8 to Tables 1 and 2 wavelet decomposition could improve the performance of forecasting. In respect of detail components, there is only one case which improves the forecasting performance. Furthermore, that improvement was only less than 0.1%. These results indicate that including the detail components in a forecasting model is not helpful in the perspective of forecasting power.


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

Jin J, Kim J - PLoS ONE (2015)

One-step forecasting results for wavelets combined with ANN and wavelets combined with ARIMA.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0142064.g008: One-step forecasting results for wavelets combined with ANN and wavelets combined with ARIMA.
Mentions: Figs 11 and 12 present the results for “adjusted forecasting.” Comparing them to Figs 8 and 10, we can see that the results no longer represent a smoothed version of the original data. When comparing Tables 5 and 6 to Tables 7 and 8, it becomes clear that there is a considerable overestimation of forecasting when the boundary condition is not considered. Although the forecasting performance decreases when the boundary condition is taken into account, based on comparing the results of Tables 7 and 8 to Tables 1 and 2 wavelet decomposition could improve the performance of forecasting. In respect of detail components, there is only one case which improves the forecasting performance. Furthermore, that improvement was only less than 0.1%. These results indicate that including the detail components in a forecasting model is not helpful in the perspective of forecasting power.

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.