44 citations · 76 across the 5 of their papers we have counts for
6 papers · 1 filter
IND-Eval: Aligning Evaluation and Simulation for Industrial Time Series Foundation Models
Vebjørn Haugland, Pavel Gulin, David Foss +5
We introduce IND-Eval, a public benchmark that organizes industrial time series across domains into source-specific forecasting tasks. Relative to established benchmarks such as GI…
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…
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…
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…
Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation
Johan Vik Mathisen, Erlend Lokna, Daesoo Lee +1
State-of-the-art approaches in time series generation (TSG), such as TimeVQVAE, utilize vector quantization-based tokenization to effectively model complex distributions of time se…
Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling
Daesoo Lee, Sara Malacarne, Erlend Aune
We present a novel time series anomaly detection method that achieves excellent detection accuracy while offering a superior level of explainability. Our proposed method, TimeVQVAE…