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

Constantinos S. Hilas; Ioannis T. Rekanos; Paris Ast. Mastorocostas

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

机译：使用高阶统计量的时间序列变化点检测：一种启发式方法

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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.

机译：时间序列水平的变化通常归因于影响其时间演变的干预。所得时间序列称为中断时间序列，可用于识别导致干预的事件并量化其影响。本文提出一种启发式的时间序列水平变化检测方法。该方法使用高阶统计量（即偏度和峰度），并且可以识别时间序列级别的变化是否存在以及发生的时间实例。该技术可直接应用于时间序列中的异常值检测，并有望具有多种应用。该方法已在模拟数据和实际数据中进行了测试，并与其他流行的变更检测技术进行了比较。

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《Mathematical Problems in Engineering》 |2013年第7期|317613.1-317613.10|共10页
作者
Constantinos S. Hilas; Ioannis T. Rekanos; Paris Ast. Mastorocostas;
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Department of Informatics and Communications, Technological Educational Institute of Serres,Terma Magnisias, 62124 Serres, Greece;

Physics Division, School of Engineering, Aristotle University of Thessaloniki, 54124 Ihessaloniki, Greece;

Department of Informatics and Communications, Technological Educational Institute of Serres,Terma Magnisias, 62124 Serres, Greece;

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机译：使用高阶统计来改变时间序列中的点检测：启发式方法

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

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