Diagnosing Shocks in Time Series
基于状态空间形式和卡尔曼滤波平滑器，提出一套高效诊断时间序列中异常行为的方法，可检测离群值、水平移位和切换等常见偏离，并给出新诊断统计量。
Abstract Efficient means of modeling aberrant behavior in times series are developed. Our methods are based on state-space forms and allow test statistics for various interventions to be computed from a single run of the Kalman filter smoother. The approach encompasses existing detection methodologies. Departures commonly observed in practice, such as outlying values, level shifts, and switches, are readily dealt with. New diagnostic statistics are proposed. Implications for structural models, autoregressive integrated moving average models, and models with explanatory variables are given.