The detection of outliers in a time series is an important issue because their presence may have serious negative effects on the analysis in many different ways. Moreover the presence of a complex seasonal pattern in the series could affect the properties of the usual outlier detection procedures. Therefore modelling the appropriate form of seasonality is a very important step when outliers are present in a seasonal time series. In this paper we present some procedures for detection and estimation of additive outliers when parametric seasonal models, in particular periodic autoregressive, are specified to fit the data. A simulation study is presented to evaluate the benefits and the drawbacks of the proposed procedure on a selection of seasonal time series. An application to three real time series is also examined.

中文翻译：

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季节时间序列中附加异常值的检测和估计

时间序列中离群值的检测是一个重要的问题，因为它们的存在可能以许多不同的方式对分析产生严重的负面影响。此外，序列中复杂季节模式的存在可能会影响常规异常值检测程序的属性。因此，当季节性时间序列中存在异常值时，对适当形式的季节性形式进行建模是非常重要的一步。在本文中，当指定参数季节性模型（特别是周期性自回归）以拟合数据时，我们提出了一些检测和估计加法离群值的程序。提出了一个仿真研究，以评估在选择季节性时间序列时所建议程序的优缺点。还研究了三个实时序列的应用。