• Martin Macaš, Kyriaki Saiti, Lenka Lhotská, Kateřina Štechová, and Pavlína Piťhová.
• Czech Insitute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Czech Republic.
• Stud Health Technol Inform. 2020 Sep 4; 273: 149-154.

AbstractThe paper compares two approaches to multi-step ahead glycaemia forecasting. While the direct approach uses a different model for each number of steps ahead, the iterative approach applies one one-step ahead model iteratively. Although it is well known that the iterative approach suffers from the error accumulation problem, there are no clear outcomes supporting a proper choice between those two methods. This paper provides such comparison for different ARX models and shows that the iterative approach outperformed the direct method for one-hour ahead (12-steps ahead) forecasting. Moreover, the classical linear ARX model outperformed more complex non-linear versions for training data covering one-month period.

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