An analytics framework for graph-based anomaly detection in healthcare time series

Anomaly detection in time series plays a vital role in diverse domains such as healthcare, finance, and industrial monitoring, where identifying deviations from normal behavior can signal critical events. While traditional methods often focus on univariate time series and assume fixed temporal dynamics, real-world systems are typically multivariate and characterized by complex interdependencies. Ignoring these relationships can lead to suboptimal detection of system-level anomalies. This paper proposes a novel graph-based framework for multivariate time series anomaly detection that explicitly captures temporal patterns and structural relationships among variables. Individual univariate time series are first transformed into Horizontal Visibility Graphs (HVGs), which are then combined into multiplex networks to preserve inter-layer interactions. Additionally, we construct feature-based similarity graphs derived from statistical properties of the series to model inter-series relations. Anomalies are identified by comparing the neighborhood structure of each series against a historical reference set, enabling the detection of subtle and coordinated deviations. Computational experiments on real-world healthcare data illustrate the behavior and practical relevance of the proposed approach in capturing complex anomalies, offering a robust and interpretable alternative to traditional techniques.