Abstract
In this paper two popular time series methods for modeling seasonality in tourism forecasts are compared. The first uses a decomposition methodology to estimate seasonal variation. In this method seasonal variation is estimated with a ratio-to-centered moving average approach. Three different approaches in calculating the seasonal indices are analyzed. The deseasonalized series are then forecast using an ARIMA model. The second methodology uses a multiplicative seasonal ARIMA (SARIMA) approach to simultaneously model trend and seasonal variations. The two methodologies are compared and the accuracy and managerial advantages of each are discussed.
Original language | American English |
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State | Published - Apr 1 2012 |
Event | Allied Academies International Conference - Duration: Apr 1 2012 → … |
Conference
Conference | Allied Academies International Conference |
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Period | 04/1/12 → … |
Keywords
- Forecasting
- Tourism
- ARIMA
- SARIMA
- Travel
DC Disciplines
- Business Administration, Management, and Operations
- Operations and Supply Chain Management