PGRF-Net: A Prototype-Guided Relational Fusion Network for Diagnostic Multivariate Time-Series Anomaly Detection

Jahoon Jeong, Hyunsoo Yoon

Keywords: Multivariate Timeseries Anomaly Detection, Time-Series Diagnostics, Prototype Learning, Relational Time-Series Modeling

TL;DR: PGRF-Net: A 2-stage unsupervised MTSAD model. It generates 4 prototype/relational evidence types, adaptively fused for detection. Achieves SOTA performance & provides diagnostic scores to aid root cause analysis.

Abstract: Multivariate time-series anomaly detection (MTSAD) faces a critical trade-off between detection performance and model transparency. We propose PGRF-Net, a novel architecture designed to achieve competitive performance while providing structured evidence to support diagnostic insights. At its core, PGRF-Net uses a Multi-Faceted Evidence Extractor that combines prototype learning with the discovery of dynamic relational structures between variables. This extractor generates four distinct types of anomaly evidence: predictive deviation, structural changes in learned variable dependencies, contextual deviation from normal-behavior prototypes, and the magnitude of localized spike events. This evidence is then processed by a Gated Evidence Fusion Network, which learns to weigh each source via data-driven gating. PGRF-Net is trained via a two-stage unsupervised strategy for robust extractor learning and subsequent fusion tuning. Extensive experiments on five public MTSAD benchmarks demonstrate its competitive or superior detection performance. Importantly, by decomposing the final anomaly score into these four evidence types, our model facilitates diagnostic analysis, offering a practical step towards more interpretable, evidence-based MTSAD.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Submission Number: 7687