Level change detection in time series using higher order statistics

Changes in the level of a time series are usually attributed to an intervention that interrupts its evolution. The resulting time series are referred to as interrupted time series and they are studied in order to measure, e.g. the impact of new laws or medical treatments. In the present paper a heuristic method for level change detection in non-stationary time series is presented. The method uses higher order statistics, namely the skewness and the kurtosis, and can identify both the existence of a change in the level of the time series as well as the time point it has happened. The technique is tested with both simulated and real world data and is straightforward applicable to the detection of outliers in time series.