Forecasting Tourism Demand
'Forecasting tourism demand' is a text that no tourism professional can afford to be without. The tourism industry has experienced an overwhelming boom over recent years, and being able to predict future trends as accurately as possible is vital in the struggle to stay one step ahead of the competition.
Building on the success of 'Practical Tourism Forecasting' this text looks at 13 methods of forecasting and with a user friendly style, 'Forecasting Tourism Demand' guides the reader through each method, highlighting its strengths and weaknesses and explaining how it can be applied to the tourism industry.
'Forecasting Tourism Demand' employs charts and tables to explain how to:
* plan a forecasting project
* analyse time series and other information
* select the appropriate forecasting model
* use the model for forecasting and evaluate its results
Ideal for marketing managers and strategic planners in business, transportation planners and economic policy makers in government who must project demand for their products among tourists. Executives who rely on forecasts prepared by others will find it invaluable in assisting them to evaluate the validity and reliability of predictions and forecasts. Those engaged in analysing business trends will find it useful in surveying the future of what has been called the largest industry in the world.
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2 Alternative forecasting methods and evaluation
3 The tourism forecasting process
4 Basic extrapolative models and decomposition
5 Intermediate extrapolative methods
6 An advanced extrapolative method
structural econometric models
9 Qualitative forecasting methods
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accurate forecasts actual value ARIMA model Box—Jenkins approach cent Chapter column consumer correlation countries data points data series Delphi method demand in Washington dependent variable destination differencing disposable personal income dummy variables estimates example explanatory variables exponential smoothing extrapolative F-statistic Figure ﬂows forecast error forecast series forecast value forecast variable forecasting model forecasting tourism demand future hotel/motel demand hotel/motel room demand indicates interquartile range lagged Makridakis MAPE marketing measure monthly months moving average model multicollinearity naive model parameters partial autocorrelations period phase potential explanatory variables predict prediction interval produce qualitative forecasting real disposable personal regression analysis regression equation regression model relationships room rate seasonal patterns series methods series of hotel/motel significant slope coefficient smoothing constant stationary statistical Table tourism demand forecasting tourism demand series tourism forecasting transformed series turning point valid variance visitor volume Witt and Witt World Tourism Organization zero