Detection of Emergent Anomalous Structure in Functional Data

Austin, Edward and Eckley, Idris A. and Bardwell, Lawrence (2024) Detection of Emergent Anomalous Structure in Functional Data. Technometrics, 66 (4). pp. 614-624. ISSN 0040-1706

Full text not available from this repository.

Abstract

Motivated by an example arising from digital networks, we propose a novel approach for detecting the emergence of anomalies in functional data. In contrast to classical functional data approaches, which detect anomalies in completely observed curves, the proposed approach seeks to identify anomalies sequentially as each point on the curve is received. The new method, the Functional Anomaly Sequential Test (FAST), captures the common profile of the curves using Principal Differential Analysis and uses a form of CUSUM test to monitor a new functional observation as it emerges. Various theoretical properties of the procedure are derived. The performance of FAST is then assessed on both simulated and telecommunications data.

Item Type:
Journal Article
Journal or Publication Title:
Technometrics
Uncontrolled Keywords:
Research Output Funding/yes_externally_funded
Subjects:
?? yes - externally fundedyesmodelling and simulationapplied mathematicsstatistics and probability ??
ID Code:
218821
Deposited By:
Deposited On:
29 Apr 2024 10:55
Refereed?:
Yes
Published?:
Published
Last Modified:
28 Nov 2024 15:00