A comparison of three different approaches to tourist arrival forecasting

By Vincent Cho. 2003

340 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Tourism, Leisure and Hospitality Management

View Paper: https://www.semanticscholar.org/paper/8c859e86bb808bc82e0b9b6c31ebe1e058df638e

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In Sociology and Political Science

In Tourism, Leisure and Hospitality Management

Citations in the corpus

Artificial intelligence in business: State of the art and future research agenda (2020, background): “This topic is attributed to studies that use AI algorithms to analyze statistical time series models and grey theory in predicting tourist arrival, consumer behavior, and air quality, contributing for market planning (e.g., Raghunathan, 1994; Cho, 2003; Yu & Schwartz, 2006; Chen, Su, Cheng, & Chiang”
Modelling and Forecasting Australian Domestic Tourism (2006, background): “Law (2000), Burger, Dohnal, Kathrada, and Law (2001), Lim and McAleer (2001b) and Cho (2001, 2003) directly implement exponential smoothing methods to forecast tourism demand.”
The impact of COVID-19 on tourism sector in India (2020, methodology): “Cho (2003) investigates the application of three forecasting techniques: exponential smoothing, ARIMA and ANN to predict the inbound demand of Hong Kong and found that ANN is the best method, specifically for unclear data patterns.”
Tourism demand modelling and forecasting—A review of recent research (2008, result): “Similar findings were also obtained by Cho (2003) who concluded that the ANN model outperforms the exponential smoothing and ARIMA models in modelling and forecasting the demand for Hong Kong tourism.”
Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques (2015, positive): “Cho (2003) also use neural network models and compares them against ARIMA and”
Tourism demand for Italy and the business cycle (2010, methodology): “(2005) count 23 studies targeted at comparing forecasts (see in particular Cho, 2003; Chu, 1998; Martin & Witt, 1989; Song & Witt, 2006; Witt, Song, & Louvieris, 2003 and references therein). Although comparisons are usually limited to complex models, it has been shown that naı̈ve models usually pro”
Forecasting of Hong Kong airport's passenger throughput (2014, background): “Tourist demand and the international air cargo hub status of HKIA has already been the subject of many prior studies (Cheng, 2011; Cho, 2003; Hiemstra & Wong, 2002; Wang & Cheng, 2010; Zhang, 2003; Zhang et al., 2004).”