TY - JOUR
T1 - A non-linear tourism demand forecast combination model
AU - Cang, Shuang
PY - 2011/2/1
Y1 - 2011/2/1
N2 - It has been demonstrated in the tourism literature that a combination of individual tourism forecasting models can provide better performance than individual forecasting models. However, the linear combination uses only inputs that have a linear correlation to the actual outputs. This paper proposes a non-linear combination method using multilayer perceptron neural networks (MLPNN), which can map the non-linear relationship between inputs and outputs. UK inbound tourism quarterly arrivals data by purpose of visit are used for this case study. The empirical results show that the proposed nonlinear MLPNN combination model is robust, powerful and can provide better performance at predicting arrivals than linear combination models.
AB - It has been demonstrated in the tourism literature that a combination of individual tourism forecasting models can provide better performance than individual forecasting models. However, the linear combination uses only inputs that have a linear correlation to the actual outputs. This paper proposes a non-linear combination method using multilayer perceptron neural networks (MLPNN), which can map the non-linear relationship between inputs and outputs. UK inbound tourism quarterly arrivals data by purpose of visit are used for this case study. The empirical results show that the proposed nonlinear MLPNN combination model is robust, powerful and can provide better performance at predicting arrivals than linear combination models.
KW - Autoregressive integrated moving average
KW - Combination forecasts
KW - Multilayer perceptron neural networks
KW - Support vector regression neural networks
KW - Tourism demand forecasting
KW - Winters' multiplicative exponential smoothing
U2 - 10.5367/te.2011.0031
DO - 10.5367/te.2011.0031
M3 - Article
AN - SCOPUS:79551569986
VL - 17
SP - 5
EP - 20
JO - Tourism Economics
JF - Tourism Economics
SN - 1354-8166
IS - 1
ER -