Monitoring for outliers and level shifts in kalman filter implementations of exponential smoothing

This paper presents a new application of a Kalman filter implementation of exponential smoothing with monitoring for outliers and level shifts. The assumption is that each observation comes from one of three models: steady, outlier, or level shift. This concept was introduced as a multiprocess model by Harrison and Stevens (1976). However, their handling of the models is different. In this paper four different model‐selection criteria are introduced and compared by applying them to data. The new features of the application include the four model‐selection criteria and the estimation of the required parameters by maximum likelihood.