Forecasting Natural Gas Spot Prices with Machine Learning

• Main Authors: Dimitrios Mouchtaris, Emmanouil Sofianos, Periklis Gogas, Theophilos Papadimitriou
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Energies
• Subjects: natural gas; spot price; machine learning; forecasting
• Online Access: https://www.mdpi.com/1996-1073/14/18/5782

The ability to accurately forecast the spot price of natural gas benefits stakeholders and is a valuable tool for all market participants in the competitive gas market. In this paper, we attempt to forecast the natural gas spot price 1, 3, 5, and 10 days ahead using machine learning methods: support vector machines (SVM), regression trees, linear regression, Gaussian process regression (GPR), and ensemble of trees. These models are trained with a set of 21 explanatory variables in a 5-fold cross-validation scheme with 90% of the dataset used for training and the remaining 10% used for testing the out-of-sample generalization ability. The results show that these machine learning methods all have different forecasting accuracy for every time frame when it comes to forecasting natural gas spot prices. However, the bagged trees (belonging to the ensemble of trees method) and the linear SVM models have superior forecasting performance compared to the rest of the models.