Forecasting tourism demand with ARMA-based methods.
By Fong-Lin Chu. 2009
212 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Management Science and Operations Research, Tourism, Leisure and Hospitality Management
View Paper: https://www.semanticscholar.org/paper/346dc57a2840f2e650299e65bbdacccb2f743708
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Hisham El-Shishiny
- Kwok Leung Tsui
- Amir F. Atiya
- Yunjie Wei
- Shouyang Wang
- Xianduan Yang
- Robert Andrawis
- Shaolong Sun
In Management Science and Operations Research
- Amir F. Atiya
- Hisham El-Shishiny
- Kwok Leung Tsui
- Shouyang Wang
- Robert Andrawis
- Souhaib Ben Taieb
- Fotios Petropoulos
- Nikolaos Kourentzes
In Tourism, Leisure and Hospitality Management
- Hisham El-Shishiny
- Kwok Leung Tsui
- Amir F. Atiya
- Yunjie Wei
- Shouyang Wang
- Xianduan Yang
- Robert Andrawis
- Shaolong Sun
Citations in the corpus
- Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques (2015, unknown): “Whilst ETS, NN, and ARFIMA are already widely used and popular, the same cannot be said of TBATS which was introduced by De Livera et al. (2011) before being incorporated in the forecast package.”
- Combination of long term and short term forecasts, with application to tourism demand forecasting (2011, methodology): “Chu [14] applied three univariate ARMA-based models to tourism demand for a number of Asian countries, and showed that these models perform very well.”
- Forecasting tourist arrivals with machine learning and internet search index (2017, background): “Tourism demand forecasting may provide basic information for subsequent planning and policy making (Chu, 2008; Witt & Song, 2002).”