3 papers
cs.LG2026
Scalable Context-Aware Graph Attention for Unsupervised Anomaly Detection in Large-Scale Mobile Networks
Sara Malacarne, Eirik Hoel-Høiseth, Erlend Aune +2
Mobile network operators must monitor thousands of heterogeneous network elements across the radio access network and the packet core, each exposing high-dimensional KPI time serie…
cs.LG2026
Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection
Sara Malacarne, Eirik Hoel-Høiseth, Erlend Aune +2
We propose C-MTAD-GAT, an \emph{unsupervised}, \emph{context-aware} graph-attention model for anomaly detection in multivariate time series from mobile networks. C-MTAD-GAT combine…
cs.LG2025
Closing the Gap Between Synthetic and Ground Truth Time Series Distributions via Neural Mapping
Daesoo Lee, Sara Malacarne, Erlend Aune
In this paper, we introduce Neural Mapper for Vector Quantized Time Series Generator (NM-VQTSG), a novel method aimed at addressing fidelity challenges in vector quantized (VQ) tim…