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

Oscar Claveria, Enric Monte () and Salvador Torra ()
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Enric Monte: Polytechnic University of Catalunya
Salvador Torra: Faculty of Economics, University of Barcelona

No 201502, IREA Working Papers from University of Barcelona, Research Institute of Applied Economics

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-for, nep-ore and nep-tur
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Working Paper: Multiple-input multiple-output vs. single-input single-output neural network forecasting (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:ira:wpaper:201502

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