Claveria, Radial basis Tourist Correlations Monthly - Multi-layer Radial basis Monte, & function arrivals to in the 1/2001 - perceptron and function Torra (2015) neural Catalonia evolution of 7/2012 Elman neural (1) networks inbound recursive networks neural performs tourism networks better demand Claveria, Machine Monthly - Neural SVR with a Torra, & learning tourist 1/1999 to Network (NN), Gaussian Monte (2016) techniques - arrivals to 3/2014 ARMA radial basis (2) Support key Spanish function Vector regions kernel Regression performs (SVR) better than the other models Claveria, Extension of Tourist Monthly data Standard Extension of the Gaussian arrivals to - 1/1999 - neural Gaussian Monte, & Torra (2016) process all Spanish in tourist 3/2014 network outperforms (3) regression regions arrivals to the model for all markets benchmark forecasting Discrete Thailand Relative NARX and Kummong & Supratid Wavelet tourist price with other neural (2016) (4) arrivals respect to 6 network more top-ranked approaches suitable NARX - tourist (GRNN, for Combination countries Elman’s RNN of DWD and (exogenous) and BPNN) projections nonlinear neural network with exogenous input (NARX) Yu, Wang, Seasonal Inbound 1/2009 - Dendritic SA-D Gao, Tang trend tourists - 12/2015 Neural performs (2017) (5) Japan Network better integrated (DNN) moving averages with Dendritic Neural Network (SA-D model) - Combined SARIMA and DNN Claveria, Monte, & Torra (2015) (1) A way of using the common trends in tourist arrivals from different visitor markets and assessing its performance. Claveria, Torra, & Monte (2016) (3) The main contribution of this study to the previous literature on tourism demand forecasting is the evaluation of the performance of several ML techniques by means of an iterated multi-step forecasting comparison.
Claveria, Monte, & Torra (2015) (1) Radial basis function neural networks Tourist arrivals to Catalonia Correlations in the evolution of inbound tourism demand Monthly - 1/2001 - 7/2012 Multi-layer perceptron and Elman recursive neural networks Radial basis function neural networks performs better Claveria, Torra, & Monte (2016) (2) Machine learning techniques - Support Vector Regression (SVR) tourist arrivals to key Spanish regions Monthly - 1/1999 to 3/2014 Neural Network (NN), ARMA SVR with a Gaussian radial basis function kernel performs better than the other models Claveria, Monte, & Torra (2016) (3) Extension of the Gaussian process regression model for forecasting Tourist arrivals to all Spanish regions in tourist arrivals to all markets Monthly data - 1/1999 - 3/2014 Standard neural network Extension of Gaussian outperforms the benchmark Kummong & Supratid (2016) (4) Discrete Wavelet - Combination of DWD and nonlinear neural network with exogenous input (NARX) Thailand tourist arrivals Relative price with respect to 6 top-ranked tourist countries (exogenous) NARX and other neural network approaches (GRNN, Elman’s RNN and BPNN) more suitable for projections Yu, Wang, Gao, Tang (2017) (5) Seasonal trend integrated moving averages with Dendritic Neural Network (SA-D model) - Combined SARIMA and DNN Inbound tourists - Japan 1/2009 - 12/2015 Dendritic Neural Network (DNN) SA-D performs better Claveria, Monte, & Torra (2015) (1) A way of using the common trends in tourist arrivals from different visitor markets and assessing its performance. Claveria, Torra, & Monte (2016) (3) The main contribution of this study to the previous literature on tourism demand forecasting is the evaluation of the performance of several ML techniques by means of an iterated multi-step forecasting comparison. NARX
I1_2_FinalReport_F.docx