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Combination of long term and short term forecasts, with application to tourism demand forecasting

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  • Andrawis, Robert R.
  • Atiya, Amir F.
  • El-Shishiny, Hisham
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    Abstract

    Forecast combination is a well-established and well-tested approach for improving the forecasting accuracy. One beneficial strategy is to use constituent forecasts that have diverse information. In this paper we consider the idea of diversity being accomplished by using different time aggregations. For example, we could create a yearly time series from a monthly time series and produce forecasts for both, then combine the forecasts. These forecasts would each be tracking the dynamics of different time scales, and would therefore add diverse types of information. A comparison of several forecast combination methods, performed in the context of this setup, shows that this is indeed a beneficial strategy and generally provides a forecasting performance that is better than the performances of the individual forecasts that are combined. As a case study, we consider the problem of forecasting monthly tourism numbers for inbound tourism to Egypt. Specifically, we consider 33 individual source countries, as well as the aggregate. The novel combination strategy also produces a generally improved forecasting accuracy.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 27 (2011)
    Issue (Month): 3 (July)
    Pages: 870-886

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    Handle: RePEc:eee:intfor:v:27:y::i:3:p:870-886

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Time series forecasting Tourism forecasting Tourism demand Forecasting tourism in Egypt Forecast combination Exponential smoothing Holt's model Bayesian forecasting;

    References

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    Cited by:
    1. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, Elsevier, vol. 30(2), pages 291-302.
    2. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    3. Jakub Nowotarski & Eran Raviv & Stefan Trueck & Rafal Weron, 2013. "An empirical comparison of alternate schemes for combining electricity spot price forecasts," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Technology HSC/13/07, Hugo Steinhaus Center, Wroclaw University of Technology.

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