Compare methods

	Tourism Demand Forecasting	Tourism Seasonality Index
Field	Tourism Hospitality	Tourism Hospitality
Family	Regression model	Regression model
Year of origin	2008	2001
Originator	Haiyan Song; Gang Li; Stephen F. Witt	Svend Lundtorp; Anastassios Tsitouras
Type	Predictive time-series and econometric demand models	Descriptive concentration index for seasonal demand
Seminal source	Song, H., & Li, G. (2008). Tourism demand modelling and forecasting - A review of recent research. Tourism Management, 29(2), 203-220.	Lundtorp, S. (2001). Measuring Tourism Seasonality. In T. Baum & S. Lundtorp (Eds.), Seasonality in Tourism (pp. 23-50). Oxford: Pergamon/Elsevier. ISBN: 9780080436746
Aliases	Tourist Arrivals Forecasting, SARIMA Tourism Forecasting, Tourism Demand Modelling and Forecasting, Econometric Tourism Forecasting	Tourism Seasonality Measurement, Seasonality Gini Coefficient, Seasonal Concentration Index, Tourism Seasonality Ratio
Related	4	4
Summary	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.	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.