Tourism Demand Forecasting - Exponential Smoothing
Methodology  -  Lake State Examples - Other Examples         
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Chen, R. J., P. Bloomfield and J. S. Fu. 2003. "An Evaluation of Alternative Forecasting Methods to Recreation Visitation." Journal of Leisure Research 35(4):441-454.

This study examines the advantages and disadvantages of basic, intermediate and advanced methods for visitor use forecasting were seasonality and limited data are characteristics of the estimation problem. The monthly use rates at the Milwaukee County Zoo are used to illustrate the seasonal time series techniques. Forecasting methods evaluated include: two naive techniques, a single moving average (SMA) with the classical decomposition procedure, single exponential smoothing (SES), double exponential smoothing (DES, Winter's, and the seasonal autoregressive integrated moving average (SARIMA). SARIMA and SMA are found to the most appropriate methods in this case study. A useful comparative table is included listing the advantages and disadvantages of each method for predicting seasonal visitor patterns depending on the quality and characteristics of the data available for analysis.

Lake States Examples:

Other Examples:

Lim, C. and M. McAleer. 2001. "Forecasting Tourist Arrivals." Annals of Tourism Research 28(4):965-977.

This study evaluates various exponential smoothing models for accuracy in predicting quarterly tourist arrivals to Australia. The Holt-Winters Additive and Multiplicative Seasonal models outperform the Single, Double and the Holt-Winters Non-Seasonal Exponential Smoothing models in forecasting. The results of this paper show that one should be concerned about seasonality in forecasting and that, in this case, the existence of unit roots does not seem to be an important issue.

Tideswell, C., T. Mules and B. Faulkner. 2001. "An Integrative Approach to Tourism Forecasting: A Glance in the Rearview Mirror." Journal of Travel Research 40(November 2001):162-171.

This study evaluated the results of tourism forecasting exercise in South Australia for the period of 1996-1998. An integrated times-series and Delphi process was used to forecast tourism demand from both the international and domestic market. Three methods of generating time-series forecasts were used: Holt's exponential smoothing; a "naive" method using average annual rate of change from the past 11 years; and a linear trend using regression analysis. A group of 26 tourism industry practitioners responded to a Delphi survey and provided comments in a follow-up group meeting process. The final forecasting method used was based on the results of the Delphi process. Grouped forecasting trends (e.g. all international visitors) were quite accurate, but forecasts for segments of the market (e.g. Japanese visitors) were not as accurate. This was particularly true for small market segments. Over all, the "naive" approach to demand forecasting was most accurate, confirming the results of earlier studies.

Martin, C. A. and S. F. Witt. 1989. "Accuracy of Econometric Forecast in Tourism." Annals of Tourism Research 16:407-428.

This paper presents the results of a study which compared the accuracy of several econometric forecasting models. The models evaluated include a log-linear econometric model, two "naive" models, an exponential smoothing model, a trend curve analysis model, a Gompertz trend curve model and a step-wise autoregression model. Each model was used to predict the travel flows of international tourists 1 and 2 years in the future. A naive "no-change" model was found to be more accurate than econometrics in 70% of the cases.

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