Document type
Working paperPublication date
Publication license
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/61327
Multiple-input multiple-output vs. single-input single-output neural network forecasting
Journal Title
Director/Tutor
Journal ISSN
Volume Title
Related resource
Abstract
This study attempts to improve the forecasting accuracy of tourism demand by using the existing common trends in tourist arrivals form all visitor markets to a specific destination in a multiple-input multiple-output (MIMO) structure. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that takes into account the correlations in the evolution of inbound international tourism demand to Catalonia (Spain). We find that the MIMO approach does not outperform the forecasting accuracy of the networks when applied country by country, but it significantly improves the forecasting performance for total tourist arrivals. When comparing the forecast accuracy of the different models, we find that radial basis function networks outperform multilayer-perceptron and Elman networks.
Subject (English)
Citation
Citation
CLAVERÍA GONZÁLEZ, Óscar, MONTE MORENO, Enric and TORRA PORRAS, Salvador. Multiple-input multiple-output vs. single-input single-output neural network forecasting. IREA – Working Papers. 2015. Vol. IR15/02. ISSN 2014-1254. [consulted: 16 of June of 2026]. Available at: https://hdl.handle.net/2445/61327