AbstractThis paper expands the fields of application of combined Bootstrap aggregating (Bagging) and Holt Winters methods to the air transportation industry, a novelty in literature, in order to obtain more accurate demand forecasts. The methodology involves decomposing the time series into three adding components: trend, seasonal and remainder. New series are generated by resampling the Remainder component and adding back the trend and seasonal ones. The Holt Winters method is used to modelling each time series and the final forecast is obtained by aggregating the forecasts set. The approach is tested using data series from 14 countries and the results are compared with five methodology benchmarks (SARIMA, Holt Winters, ETS, Bagged.BLD.MBB.ETS and Seasonal Naive) using Symmetric Mean Absolute Percentage Error (sMAPE). The empirical results obtained with Bagging Holt Winters methods consistently outperform the benchmarks by providing forecasts that are more accurate.
HighlightsBagging Holt Winters method is used to forecast the air transportation demand.Bagging Holt Winters method is a novelty in the field of air transportation.Both additive and multiplicative seasonality are considered.Comparison to SARIMA, Holt Winters, ETS, Bagged.BLD.MBB.ETS and Seasonal Naïve.The method outperformed all benchmark: sMAPE was reduced in 14 country time series.
Air transportation demand forecast through Bagging Holt Winters methods
Journal of Air Transport Management ; 59 ; 116-123
2016-12-05
8 pages
Article (Journal)
Electronic Resource
English
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