Abstract
Natural gas is often described as the cleanest fossil fuel. The consumption of natural gas is increasing rapidly. Accurate prediction of natural gas spot prices would significantly benefit energy management, economic development, and environmental conservation. In this study, the least squares regression boosting (LSBoost) algorithm was used for forecasting natural gas spot prices. LSBoost can fit regression ensembles well by minimizing the mean squared error. Henry Hub natural gas spot prices were investigated, and a wide range of time series from January 2001 to December 2017 was selected. The LSBoost method is adopted to analyze data series at daily, weekly and monthly. An empirical study verified that the proposed prediction model has a high degree of fitting. Compared with some existing approaches such as linear regression, linear support vector machine (SVM), quadratic SVM, and cubic SVM, the proposed LSBoost-based model showed better performance such as a higher R-square and lower mean absolute error, mean square error, and root-mean-square error.
Highlights
As a low carbon and eco-efficient energy resource, natural gas has become an energy option to mitigate the environmental impacts of traditional fossil fuels for human beings
The results show that least squares regression boosting (LSBoost) can achieve the highest R-square and lowest mean absolute error (MAE), Mean square error (MSE), and Root-mean-square error (RMSE) among all the methods
As reported by Malliarisa, the nonlinear methods were best for crude oil, heating oil, gasoline, and natural gas forecasting, even though both linear and nonlinear techniques are used in forecasting interrelated energy product prices [51]
Summary
As a low carbon and eco-efficient energy resource, natural gas has become an energy option to mitigate the environmental impacts of traditional fossil fuels for human beings. Natural gas is one of the most important energy resources in the world, and it fulfils more than one-fifth of energy demand worldwide [1]. Natural gas is extensively used for residences, commerce, transportation, electric power, and industrial production. Natural gas price forecasting is increasingly playing an essential role for stakeholder groups highly sensitive to price fluctuation [2]. The forecast has widespread applications in all walks of life. Accurate natural gas price forecasting of one, three, five, or more months into the future is extremely significant in energy management, economic development, and environmental conservation
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