This paper considers the problem of outlier detection in functional data analysis focusing particularly on the more difficult case of shape outliers. We present an inductive conformal anomaly detection method based on elastic functional distance metrics. This method is evaluated and compared to similar conformal anomaly detection methods for functional data using simulation experiments. The method is also used in the analysis of two real exemplar data sets that show its utility in practical applications. The results demonstrate the efficacy of the proposed method for detecting both magnitude and shape outliers in two distinct outlier detection scenarios.

翻译：本文探讨函数型数据分析中的异常值检测问题，特别关注更具挑战性的形状异常情形。我们提出一种基于弹性函数距离度量的归纳式保形异常检测方法。通过仿真实验，对该方法与函数型数据领域其他类似保形异常检测方法进行了评估比较。此外，该方法被应用于两个真实示例数据集的分析，展示了其在实际应用中的有效性。研究结果表明，在两种不同的异常检测场景中，所提方法对幅度异常与形状异常均具有优异的检测效能。