Multiple-input multiple-output vs. single-input single-output neural network forecasting

Oscar Claveria, Enric Monte () and Salvador Torra ()
Additional contact information
Enric Monte: Department of Signal Theory and Communications. Polytechnic University of Catalunya.
Salvador Torra: Department of Econometrics & Riskcenter-IREA. Universitat de Barcelona

No 201502, AQR Working Papers from University of Barcelona, Regional Quantitative Analysis Group

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.

Keywords: Tourism demand; forecasting; multivariate; multiple-output; artificial neural networks JEL classification: C22; C45; C63; L83; R11 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2015-01, Revised 2015-01
New Economics Papers: this item is included in nep-cmp, nep-ets, nep-for, nep-ore and nep-tur
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Multiple-input multiple-output vs. single-input single-output neural network forecasting (2015)
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