Methoden vergelijken

	Tourism Seasonality Index	Tourism Demand Forecasting
Vakgebied	Tourism Hospitality	Tourism Hospitality
Familie	Regression model	Regression model
Jaar van ontstaan	2001	2008
Grondlegger	Svend Lundtorp; Anastassios Tsitouras	Haiyan Song; Gang Li; Stephen F. Witt
Type	Descriptive concentration index for seasonal demand	Predictive time-series and econometric demand models
Oorspronkelijke bron	Lundtorp, S. (2001). Measuring Tourism Seasonality. In T. Baum & S. Lundtorp (Eds.), Seasonality in Tourism (pp. 23-50). Oxford: Pergamon/Elsevier. ISBN: 9780080436746	Song, H., & Li, G. (2008). Tourism demand modelling and forecasting - A review of recent research. Tourism Management, 29(2), 203-220.
Aliassen	Tourism Seasonality Measurement, Seasonality Gini Coefficient, Seasonal Concentration Index, Tourism Seasonality Ratio	Tourist Arrivals Forecasting, SARIMA Tourism Forecasting, Tourism Demand Modelling and Forecasting, Econometric Tourism Forecasting
Verwant	4	4
Samenvatting	Tourism seasonality measurement summarizes how unevenly tourism demand is distributed across the year. Destinations rarely receive visitors at a constant rate; arrivals, overnight stays, and revenue cluster in peak months and thin out in the off-season, straining capacity at the top and leaving resources idle at the bottom. Seasonality indices turn a monthly demand series into a single, comparable number measuring this temporal concentration. Simple ratios compare the peak month to the average or to the trough, while the Gini coefficient — long established in the study of inequality and adapted by Svend Lundtorp and others to tourism — captures concentration across all months at once via a Lorenz curve. Adjusted versions, such as Tsitouras's 'months equivalent' degree of seasonality, make the index easier to interpret and compare.	Tourism demand forecasting predicts future tourist arrivals, overnight stays, or expenditure from historical data, supporting planning by destinations, airlines, hotels, and policymakers. The field spans two broad model families. Time-series models such as seasonal ARIMA (SARIMA) extrapolate the patterns embedded in the demand series itself — trend, seasonality, and autocorrelation — without explanatory variables. Econometric models such as autoregressive distributed lag models (ADLM) and error-correction models relate demand to drivers like income, relative prices, and exchange rates, allowing both forecasting and policy analysis. Haiyan Song and Gang Li's influential 2008 review in Tourism Management synthesized this literature, documenting the proliferation of methods since 2000 and emphasizing rigorous out-of-sample evaluation. Their work, with Stephen Witt, helped make tourism demand forecasting a methodologically mature subfield.