The tourism forecasting competition

George Athanasopoulos (), Rob J Hyndman (), Haiyan Song () and Doris C Wu

No 10/08, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We evaluate the performance of various methods for forecasting tourism demand. The data used include 380 monthly series, 427 quarterly series and 530 yearly series, all supplied to us by tourism bodies or by academics from previous tourism forecasting studies. The forecasting methods implemented in the competition are univariate time series approaches, and also econometric models. This forecasting completion differs from previous competitions in several ways: (i) we concentrate only on tourism demand data; (ii) we include econometric approaches; (iii) we evaluate forecast interval coverage as well as point forecast accuracy; (iv) we observe the effect of temporal aggregation on forecasting accuracy; and (v) we consider the mean absolute scaled error as an alternative forecasting accuracy measure.

Keywords: Tourism forecasting; ARIMA; Exponential smoothing; Time varying parameter model; Autoregressive distributed lag model; Vector autoregression (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for and nep-tur
Date: 2008-12, Revised 2009-10
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