Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.01172 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912319115624448 |
|---|---|
| author | Adams, Jason Berman, Brandon Michalenko, Joshua Tucker, J. Derek |
| author_facet | Adams, Jason Berman, Brandon Michalenko, Joshua Tucker, J. Derek |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conformal Anomaly Detection for Functional Data with Elastic Distance Metrics Adams, Jason Berman, Brandon Michalenko, Joshua Tucker, J. Derek Methodology 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. |
| title | Conformal Anomaly Detection for Functional Data with Elastic Distance Metrics |
| topic | Methodology |
| url | https://arxiv.org/abs/2504.01172 |