Anomaly Detection in Spatio-Temporal Data: Theory and Application

TL;DR

This paper reviews three methods—SPC, scan statistics, and tensor decomposition—for detecting anomalies in spatio-temporal data, demonstrating their strengths and applications on satellite solar activity images.

Contribution

It provides a comparative analysis of three anomaly detection methods in spatio-temporal data and illustrates their application on real satellite imagery.

Findings

Scan statistics excel at detecting clustered anomalies.

Multivariate SPC effectively detects sparse anomalies.

Tensor decomposition identifies anomalies with specific patterns.

Abstract

This paper provides an overview of three notable approaches for detecting anomalies in spatio-temporal data. The three review methods are selected from the framework of multivariate statistical process control (SPC), scan statistics, and tensor decomposition. For each method, we first demonstrate its technical intricacies and then apply it to a real-world dataset, which is 300 images of solar activities collected by satellite. Our findings reveal that these methods possess distinct strengths. Specifically, scan statistics excel at identifying clustered anomalies, multivariate SPC is effective in detecting sparse anomalies, and tensor decomposition is adept at identifying anomalies exhibiting desirable patterns, such as temporal circularity. We emphasize the importance of customizing the selection of these methods based on the specific characteristics of the dataset and the analysis objectives.