Change Point Detection in Time Series Using Higher-Order Statistics: A Heuristic Approach

Abstract

Changes in the level of a time series are usually attributed to an intervention that affects its temporal evolution. The resulting time series are referred to as interrupted time series and may be used to identify the events that caused the intervention and to quantify their impact. In the present paper, a heuristic method for level change detection in 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 and the time instance when it has happened. The technique is straightforwardly applicable to the detection of outliers in time series and promises to have several applications. The method is tested with both simulated and real-world data and is compared to other popular change detection techniques.