Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/61327
Title: Multiple-input multiple-output vs. single-input single-output neural network forecasting
Author: Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
Keywords: Turisme
Xarxes neuronals (Informàtica)
Anàlisi multivariable
Sistemes MIMO
Tourism
Multivariate analysis
MIMO systems
Issue Date: 2015
Publisher: Universitat de Barcelona. Institut de Recerca en Economia Aplicada Regional i Pública
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.
Note: Reproducció del document publicat a: http://www.ub.edu/irea/working_papers/2015/201502.pdf
It is part of: IREA – Working Papers, 2015, IR15/002
AQR – Working Papers, 2015, AQR15/002
URI: http://hdl.handle.net/2445/61327
ISSN: 2014-1254
Appears in Collections:Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))
Documents de treball / Informes (Econometria, Estadística i Economia Aplicada)
AQR (Grup d’Anàlisi Quantitativa Regional) – Working Papers

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