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

# metadata.dc.description.abstract[].value
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.

# metadata.dc.publisher[].value
Universitat de Barcelona. Institut de Recerca en Economia Aplicada Regional i Pública

# metadata.dc.source[].value
Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))

# metadata.dc.title[].value
Multiple-input  multiple-output  vs. single-input  single-output neural network forecasting