Detecting Level Shifts in Time Series
指出传统异常检测方法（如Tsay方法）难以识别时间序列中的水平位移，并提出一种简单改进，能有效提升检测能力。
This article demonstrates the difficulty that traditional outlier detection methods, such as that of Tsay, have in identifying level shifts in time series. Initializing the outlier/level-shift search with an estimated autoregressive moving average model lowers the power of the level-shift detection statistics. Furthermore, the rule employed by these methods for distinguishing between level shifts and innovation outliers does not work well in the presence of level shifts. A simple modification to Tsay's procedure is proposed that improves the ability to correctly identify level shifts. This modification is relatively easy to implement and appears to be quite effective in practice.