A time series forecasting competition for tourism data

The tourism forecasting competition

George Athanasopoulos, Rob J Hyndman, Haiyan Song and Doris Wu

International Journal of Forecasting, to appear

註: 2010 年 9 月 20 日報名截止
Abstract We evaluate the performance of various methods for forecasting tourism demand. The data used include 366 monthly series, 427 quarterly series and 518 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 and multivariate time series approaches, and econometric models. This forecasting competition differs from previous competitions in several ways: (i) we concentrate only on tourism demand data; (ii) we include approaches with explanatory variables; (iii) we evaluate the 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. We find that pure time series approaches provide more accurate forecasts for tourism data than models with explanatory variables. For seasonal data we implement three fully automated pure time series algorithms that generate accurate point forecasts and two of these also produce forecast coverage probabilities which are satisfactorily close to the nominal rates. For annual data we find that Naïve forecasts are hard to beat.

Keywords: ARIMA, exponential smoothing, state space model, time varying parameter model, dynamic regression, autoregressive distributed lag model, vector autoregression.

If anyone can provide more accurate time series forecasts for tourism data than those in this recent paper (http://robjhyndman.com/papers/the-tourism-forecasting-competition), they could win $AUD500 and have a discussion paper in the “International Journal of Forecasting".See
http://robjhyndman.com/researchtips/tourism-forecasting-competition/
for the details.

The competition is hosted by Kaggle at http://kaggle.com/tourism1