Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

Moting Su · Zongyi Zhang · Ye Zhu · Donglan Zha · Wenying Wen

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Stock Market Forecasting Methods Forecasting Techniques and Applications Energy Load and Power Forecasting

Abstract

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.

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References

1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting 2006Another look at measures of forecast accuracy 1981ON THE VALIDATION OF MODELS 2016A new metric of absolute percentage error for intermittent demand forecasts 2016Predicting the direction of stock market prices using random forest 2017Short-term forecasting of natural gas prices using machine learning and feature selection algorithms 2019Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm

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2020Daily natural gas price forecasting by a weighted hybrid data-driven model 2023Natural gas spot price prediction research under the background of Russia-Ukraine conflict - based on FS-GA-SVR hybrid model

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