Forecasting When Pattern Changes Occur Beyond the Historical Data
提出一种新预测方法，考虑历史数据之外可能发生的模式变化，通过构建短期和长期模型并基于过去变化的数量、程度和持续时间进行调和，在M竞赛的111个序列上表现优于现有方法。
Forecasting methods currently available assume that established patterns or relationships will not change during the post-sample forecasting phase. This, however, is not a realistic assumption for business and economic series. This paper describes a new approach to forecasting which takes into account possible pattern changes beyond the historical data. This approach is based on the development of two models: one short, the other long term. These models are then reconciled to produce the final forecasts by setting certain parameters as a function of the number, extent, and duration of pattern changes that have occurred in the past. The proposed method has been applied to the 111 series used in the M-Competition. Post-sample forecasting accuracy comparisons show the superiority of the proposed approach over the most accurate methods in the M-Competition.