Forecasting natural gas price trends using random forest and support vector machine classifiers

Price forecasting using statistical modeling methods and data mining has been a topic of great interest among data scientists around the world. In this paper, different machine learning approaches are applied to forecasting future yearly price trends in the natural gas Title Transfer Facility market in the Netherlands. The study compares two models: random forest and support vector classifiers. The identification of potential natural gas price drivers that improve the model's classification is crucial. The forecast horizon was set in a range from 10 to 60 trading days, considering that shorter time horizons have greater importance for trading. The results reflect values up to 85% of the area-under-the-curve score as a reaction of the models to the four different feature combinations used. This invites continued research on the multiple opportunities that these new technologies could create.