A fractionally integrated autoregressive moving average approach to forecasting tourism demand

Abstract

The primary aim of this paper is to incorporate fractionally integrated ARMA (p, d, q) (ARFIMA) models into tourism forecasting, and to compare the accuracy of forecasts with those obtained by previous studies. The models are estimated using the volume of monthly international tourist arrivals in Singapore. Empirical findings demonstrate the evidence that the approach we propose generates relatively lower sample mean absolute percentage errors (MAPEs). This study also deals with the volatile data faced by a forecaster. We use the Asian financial crisis and the September 11 event as examples of economic and political shocks. With respect to the objective of shaving the coefficient MAPE, forecasts based on the selected ARFIMA models dominate convincingly. r

Persistence in the Short- and Long-Term Tourist Arrivals to Australia

“…This paper adopts a fractional integration model (Chu, 2008;Gil-Alana, 2005), while the traditional unit root integrated models are common in tourism (Maloney and Montes Rojas, 2005;Bhattacharya and Narayan, 2005;Lim and McAleer, 2002). Gil-Alana (2005) employed a simple seasonal fractionally integrated model (i.e., model 2), while in this paper we have shown that a model incorporating fractional integration at both the zero and the seasonal frequencies outperforms those using fractional integration either at zero or the seasonal frequencies.…”

Data Pre-Processing for Neural Network-Based Forecasting: Does It Really Matter?

“…Many authors have acknowledged the importance of applying new approaches to tourism demand forecasting in order to improve the accuracy of the methods of analysis (Song, Li 2008). The availability of more advanced forecasting techniques has led to a growing interest Artificial Intelligence (AI) models (Yu, Schwartz 2006;Goh et al 2008;Lin et al 2011;Chen 2011;Celotto et al 2012;Wu et al 2012;Cang, Yu 2014) to the detriment of time series models (Chu 2008(Chu , 2011Assaf et al 2011) and causal econometric models (Page et al 2012). Some of the new AI based techniques are fuzzy time series models (Tsaur, Kuo 2011), genetic algorithms (Hadavandi et al 2011), expert systems (Shahrabi et al 2013;Pai et al 2014) and Support Vector Machines (SVMs) (Chen, Wang 2007;Hong et al 2011).…”

A study of the non‐economic determinants in tourism demand

“…substitute prices), exchange rates, transportation cost between destination and origin, as well as dummy variables on various special events and deterministic trends (e.g. Barry and O'Hagan, 1972;Loeb, 1982;Stronge and Redman, 1982;Uysal and Crompton, 1984;Smeral, 1988;Di Matteo and Di Matteo, 1993;Crouch, 1994;Lim, 1999;Croes, 2000;Vanegas and Croes, 2000;Song et al, 2003;Chu, 2004;Li et al, 2005; Song and Witt, 2006;Wong et al, 2007;Chu, 2008;Song and Li, 2008). It postulates that factors of income and price are likely to play a central role in determining the demand for international tourism.…”