Restarting a forecasting system when demand suddenly changes
指数平滑在自动预测中常用，但重启时因历史数据有限导致权重失真。本文提出递减alpha法，通过每期改变平滑常数来保持指数权重模式，并用实例对比其他启动方法。
Abstract Exponential smoothing is commonly used in automatic forecasting systems. However, when only a small amount of historical data is relevant to future demands, the ad hoc startup methods used in exponential smoothing produce unexpected results. With large data sets, an exponentially smoothed average implicitly weights the data in a declining manner, similar to discounting. This pattern is important in that it minimizes a measure of forecast error. However, restarting with limited data distorts the weighting pattern. A new technique, termed the declining alpha method, is presented and shown to preserve the exponential weight pattern. The key is a formula that changes the smoothing constant each period. Examples are given to illustrate the method and contrast it to other startup techniques.