1. A method for detecting abnormal observations from a series of observations, comprising: formulating, by a computer, an observation prediction by applying a working exponential smoothing model to a series of past observations; determining, by the computer, whether a most recent observation is an abnormal observation by comparing the most recent observation to the observation prediction; updating, by the computer, using the most recent observation, a shadow exponential smoothing model; selectively updating, by the computer, using the most recent observation, the working exponential smoothing model based on the determining whether the most recent observation is an abnormal observation; making a determination, by the computer, that a number of recent abnormal observations exceeds a threshold number of recent abnormal observations; and upon making the determination that the number of recent abnormal observations exceeds a threshold number of consecutive abnormal observations, replacing, by the computer, the working exponential smoothing model with the shadow exponential smoothing model.