دانلود رایگان مقاله پیش بینی تقاضای حمل و نقل هوایی از طریق روش هولت وینترز باگینگ

abstract

This 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.

6. Conclusion

Given the massive application of operation research techniques in the air industry, competitive advantage among players is sought in details. The optimization of processes and the corresponding costs are discussed at decimals scale. In this context, any contribution to reach more accurate demand forecasts is welcome. This paper applies for the first time, as long as the authors are aware, a combination of the Bootstrap aggregating (Bagging) method with the exponential smoothing method Holt Winters to the air industry in order to predict future demand for air transportation. The combined methodology is designated as Bagging Holt Winters. The results obtained for prove that the methodology can improve forecast accuracy.