Tourism forecasting: To combine or not to combine?

By Kevin K. F. Wong, Haiyan Song, Stephen F. Witt, D. Wu. 2007

231 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Sociology and Political Science

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

1. D. Wu
2. Haiyan Song
3. Shujie Shen
4. Gang Li
5. Pritee Ray
6. Ratri Parida
7. Sanjita Jaipuria
8. Amir F. Atiya

In Management Science and Operations Research

1. Gang Li
2. D. Wu
3. Haiyan Song
4. Shujie Shen
5. Amir F. Atiya
6. Hisham El-Shishiny
7. Kwok Leung Tsui
8. Pritee Ray

In Sociology and Political Science

1. D. Wu
2. Haiyan Song
3. Shujie Shen
4. Gang Li
5. Bing Pan
6. Pritee Ray
7. Ratri Parida
8. Sanjita Jaipuria

Citations in the corpus

• Combination of long term and short term forecasts, with application to tourism demand forecasting (2011, background): “Song, Witt, Wong, and Wu (2009) also explored forecast combination for tourism data, and obtained even more favorable results than Wong et al. (2007).”
• Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques (2015, unknown): “Furthermore, a number of authors compare ARIMA-type models with other time-series or econometric models (see, inter alia Goh and Law, 2002; Kulendran and Witt, 2003; Chu, 2004; Kim and Moosa, 2005; Vu and Turner, 2006; Wong et al., 2007; Chu, 2008; Brida and Risso, 2011; Wan et al., 2013).”
• The impact of COVID-19 on tourism sector in India (2020, methodology): “Wong et al. (2007) forecasted tourism demand for Hong Kong from ten different countries using four different forecasting models: ARIMA, ADLM, ECM and VAR.”
• Tourism demand modelling and forecasting—A review of recent research (2008, background): “Wong et al (2007) advanced the study on forecast combination in the context of Hong Kong inbound tourism forecasting and drew a similar conclusion that combined forecasts can generally outperform the worst individual forecasts, thereby risk of complete forecast failure could be reduced through…”
• Combination forecasts of international tourism demand (2011, methodology): “This study evaluates the forecasting performance of different combination methods in the context of tourism demand.”
• Forecasting tourist arrivals with machine learning and internet search index (2017, background): “Other time series models include but are not limited to state space models (Athanasopoulos & Hyndman, 2008; Beneki, Eeckels, & Leon, 2012; Du Preez & Witt, 2003), error correction models (ECM) (Lee, 2011; Shen, Li, & Song, 2009; Vanegas, 2013; Wong et al., 2007), and generalized autoregressive condi”
• New developments in tourism and hotel demand modeling and forecasting (2017, background): “Particularly, Wong et al. (2007) demonstrated that combination forecasts cannot beat the best single forecast but always perform better than the worst single one.”
• Forecasting hotel room demand using search engine data. (2012, background): “Specifically, recent studies have demonstrated that combinations of forecasting methods can produce more accurate results in a tourism context (Chan et al. In Press; Chu 1998; Palm & Zellner 1992; Wong et al. 2007).”