activity
20232026
most citedExplainable Time Series Anomaly Detection using Masked Latent Generative Modeling

44 citations · 76 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG2026

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…

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…

cs.LG2024

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…

cs.LG2023★ 44 cited

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…

physics.geo-ph2023★ 32 cited

Latent Diffusion Model for Conditional Reservoir Facies Generation

Daesoo Lee, Oscar Ovanger, Jo Eidsvik +3

Creating accurate and geologically realistic reservoir facies based on limited measurements is crucial for field development and reservoir management, especially in the oil and gas…