A01=Douglas Frechtling
ARIMA Model
ARMA Model
Author_Douglas Frechtling
Box Jenkins Approach
Category=KCJ
Category=KNSG
demand modelling
Double Exponential Smoothing
economic impact assessment
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Exponential Smoothing
extrapolative analysis
Forecasting Tourism Demand
forecasts
Jan-88 Jul-88 Jan-89 Jul-89 Jan-90
Jul-96 Jan-97 Jul-97 Jan-98 Jul-98
Low MAPE
methods
model
Moving Average Model
NA NA
NA NA NA
NA NA NA NA
NA NA NA NA NA
Partial Autocorrelations
qualitative
Qualitative Forecasting Methods
quantitative tourism forecasting methods
RDPI
regression techniques
research
Seasonally Adjusted
series
SES
Small MAPE
smith
Smith Travel Research
strategic planning tourism
structural
Structural Econometric Models
time
Time Series Forecasting Methods
tourism analytics
Tourism Demand
Tourism Demand Series
travel
Product details
- ISBN 9780750651707
- Weight: 600g
- Dimensions: 156 x 234mm
- Publication Date: 25 May 2001
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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'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.
