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Francisco Castañeda, Markus Schicks, Sascha Niro and Niklas Hartmann
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.
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Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
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© Infopro Digital Risk (IP) Limited (2022). All rights reserved. Published by Infopro Digital Services Limited, 133 Houndsditch, London, EC3A 7BX. Companies are registered in England and Wales with company registration numbers 09232733 & 04699701.
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