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Application of the nonlinear autoregressive model with exogenous inputs for predicting the gross operating product based on usali data

• Lukasz Labanowski
• Pawel Fracz
• Daria Wotzka
• Joachim Foltys

(2024). ECMS 2024, 38th Proceedings
Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2024
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-84-5
ISBN: 978-3-937436-83-8 (CD) Communications of the ECMS Volume 38, Issue 1, June 2024, Cracow, Poland June 4th – June 7th, 2024

https://doi.org/10.7148/2024-0077

Lukasz labanowski, Pawel fracz, Daria wotzka, Joachim foltys (2024). Application of the nonlinear autoregressive model with exogenous inputs for predicting the gross operating product based on USALI data, ECMS 2024, Proceedings Edited by: Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev, European Council for Modelling and Simulation. doi:10.7148/2024-0077

This study presents an advanced data analysis for developing a predictive model for the gross operating profit time series, using monthly data from two Polish hotels compliant with the Uniform System of Accounts for the Lodging Industry (USALI). Emphasizing the USALI data, the research utilized the Nonlinear Autoregressive with eXogenous (NARX) inputs model to capture both linear and nonlinear trends. Extensive testing involved eight neural network learning methods across double layer networks with 5 to 30 neurons in the hidden layer and various sizes of delay parameter. The effectiveness of each model was measured by the mean squared error, identifying the model with its lowest value as the most accurate, and by normality of test residuals.