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本文探讨了异常检测和新颖性检测的区别，并介绍了用于识别异常值的多种统计技术，包括Tukey's fences、z-score和修改后的z-score方法。通过实例展示了如何使用时间序列数据的重采样、可视化和不同阈值来检测异常。同时，文章提到了异常检测在不同上下文中的重要性，并讨论了改变点检测在预测数据中突然变化的角色。
In addition to missing data, as discussed in Chapter 7, Handling Missing Data, a common data issue you may face is the presence of outliers. Outliers can be point outliers, collective outliers, or contextual outliers. For example,
- a point outlier occurs when a data point deviates from the rest of the population—sometimes referred to as a global outlier.
- Collective outliers集体异常值, which are groups of observations</
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