ArXiv Preprint
Sensor-based remote health monitoring is used in industrial, urban and
healthcare settings to monitor ongoing operation of equipment and human health.
An important aim is to intervene early if anomalous events or adverse health is
detected. In the wild, these anomaly detection approaches are challenged by
noise, label scarcity, high dimensionality, explainability and wide variability
in operating environments. The Contextual Matrix Profile (CMP) is a
configurable 2-dimensional version of the Matrix Profile (MP) that uses the
distance matrix of all subsequences of a time series to discover patterns and
anomalies. The CMP is shown to enhance the effectiveness of the MP and other
SOTA methods at detecting, visualising and interpreting true anomalies in noisy
real world data from different domains. It excels at zooming out and
identifying temporal patterns at configurable time scales. However, the CMP
does not address cross-sensor information, and cannot scale to high dimensional
data. We propose a novel, self-supervised graph-based approach for temporal
anomaly detection that works on context graphs generated from the CMP distance
matrix. The learned graph embeddings encode the anomalous nature of a time
context. In addition, we evaluate other graph outlier algorithms for the same
task. Given our pipeline is modular, graph construction, generation of graph
embeddings, and pattern recognition logic can all be chosen based on the
specific pattern detection application. We verified the effectiveness of
graph-based anomaly detection and compared it with the CMP and 3 state-of-the
art methods on two real-world healthcare datasets with different anomalies. Our
proposed method demonstrated better recall, alert rate and generalisability.
Nivedita Bijlani, Oscar Mendez Maldonado, Samaneh Kouchaki
2022-11-29
