Time series anomaly detection via temporal relationship graphs and adaptive smoothing
Rongfei Ma, Yuhao Ma, Xiufeng Liu · Applied Soft Computing · 2025
Anomaly detection in time series data is crucial across diverse domains yet challenging due to complex temporal dependencies and high dimensionality . Existing methods often fail to capture a holistic view of these dependencies, overlooking subtle anomalies. This paper introduces a novel framework integrating multifaceted temporal correlation modeling with efficient dimensionality reduction for comprehensive anomaly detection. We construct three distinct Temporal Correlation Graphs (TCGs) – Similarity, Causality, and Synchronization – capturing diverse temporal dependencies beyond pairwise similarity . We seamlessly incorporate Reverse Piecewise Aggregate Approximation (RPAA) within the TCG construction , reducing dimensionality while preserving essential temporal features. Our framework uses a diverse set of statistical, graph-theoretic, and temporal metrics combined with a context-aware scoring system leveraging TCG clusters, enabling accurate detection of both point-based and event-based anomalies. Extensive evaluations on real-world and synthetic datasets demonstrate superior performance, achieving up to a 17% improvement in the F1-score compared to state-of-the-art techniques across a wide range of anomaly types. The statistical significance of these improvements is confirmed through a Wilcoxon signed-rank test.