Time series bagging has been deemed an effective way to improve unstable modelling procedures and subsequent forecasting accuracy. However, the literature has paid little attention to decomposition in time series bagging. This study investigates the impacts of various decomposition methods on bagging forecasting performance. Eight popular decomposition approaches are incorporated into the time series bagging procedure to improve unstable modelling procedures, and the resulting bagging methods' forecasting performance is evaluated. Using the world's top 20 inbound destinations as an empirical case, this study generates one-to eight-step-ahead tourism forecasts and compares them against benchmarks, including non-bagged and seasonal naïve models. For short-term forecasts, bagging constructed via seasonal extraction in autoregressive integrated moving average time series decomposition outperforms other methods. An autocorrelation test shows that efficient decomposition reduces variance in bagging forecasts.

时间序列装袋被认为是改进不稳定建模程序和后续预测准确性的有效方法。然而，文献很少关注时间序列装袋中的分解。本研究调查了各种分解方法对装袋预测性能的影响。八种流行的分解方法被纳入时间序列装袋程序以改进不稳定的建模程序，并评估了由此产生的装袋方法的预测性能。本研究以世界前 20 大入境目的地为实证案例，生成提前一到八步的旅游预测，并将其与基准进行比较，包括非套袋和季节性朴素模型。对于短期预测，在自回归综合移动平均时间序列分解中通过季节性提取构建的 bagging 优于其他方法。自相关测试表明，有效的分解减少了 bagging 预测的方差。