Functional Anomaly Detection: a Benchmark Study

TL;DR

This paper evaluates recent functional data analysis techniques for anomaly detection in high-frequency sensor data, comparing their performance on real datasets and providing practical recommendations.

Contribution

It offers a comprehensive benchmark of anomaly detection methods in the functional data context, highlighting strengths and weaknesses for real-world applications.

Findings

Functional analysis methods vary in effectiveness depending on anomaly type.

Benchmark results identify the most reliable techniques for specific scenarios.

Practical guidelines are provided for practitioners deploying anomaly detection.

Abstract

The increasing automation in many areas of the Industry expressly demands to design efficient machine-learning solutions for the detection of abnormal events. With the ubiquitous deployment of sensors monitoring nearly continuously the health of complex infrastructures, anomaly detection can now rely on measurements sampled at a very high frequency, providing a very rich representation of the phenomenon under surveillance. In order to exploit fully the information thus collected, the observations cannot be treated as multivariate data anymore and a functional analysis approach is required. It is the purpose of this paper to investigate the performance of recent techniques for anomaly detection in the functional setup on real datasets. After an overview of the state-of-the-art and a visual-descriptive study, a variety of anomaly detection methods are compared. While taxonomies of abnormalities (e.g. shape, location) in the functional setup are documented in the literature, assigning a specific type to the identified anomalies appears to be a challenging task. Thus, strengths and weaknesses of the existing approaches are benchmarked in view of these highlighted types in a simulation study. Anomaly detection methods are next evaluated on two datasets, related to the monitoring of helicopters in flight and to the spectrometry of construction materials namely. The benchmark analysis is concluded by recommendation guidance for practitioners.