Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.Energies 2019, 12, 1680 2 of 17 usage of resources based on accurate predictions. Accurate natural gas price forecasting not only provides an important guide for effective implementation of energy policy and planning, but also is extremely significant in economic planning, energy investment, and environmental conservation. Therefore, researchers continue to study natural gas price forecasting models with great interest, with the aim of making predictions as accurate as possible in future.There are plenty of methods for analyzing and forecasting natural gas prices and machine learning is increasingly used. Machine learning algorithms can learn from historical relationships and trends in the data and make data-driven predictions or decisions. A great number of researchers have investigated natural gas price prediction with the aid of various machine learning methods so far. For instance, Abrishami and Varahrami mixed the data handling neural network technique with a rule-based expert system in forecasting natural gas prices [3]. Busse et al. used a nonlinear autoregressive exogenous model neural network [4]. Azadeh et al. studied a hybrid neuro-fuzzy method composed of ANN, fuzzy linear regression, and conventional regression [5]. Salehnia et al. developed several nonlinear models using the gamma test, including local linear regression, dynamic local linear regression, and ANN models [6]. Ceperic et al. proposed a strategic seasonality-adjusted, support vector regression machine-based model [7]. Su et al. utilized a least squares regression boosting algorithm in natural gas price prediction [8].As indicated in the abovementioned existing studies that exploited machine learning tools for natural gas price prediction, ANN and SVM are widely used machine learning methods in forecasting natural gas prices. In addition to ANN and SVM, this study will introduce two other common machine learning approaches, GBM and GPR (these two methods were used for forecasting hourly loads in US [9]). ...

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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.

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