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cs.LG2026
GSLAD: Prototype-Regularized Graph Structure Learning for Multivariate Time Series Anomaly Detection
Zepeng Zhang, Fuad Khuri, Keivan Faghih Niresi +1
Unsupervised multivariate time series anomaly detection methods typically identify anomalies through forecasting, reconstruction, or representation discrepancies. However, industri…
cs.LG2026
In-Context Residual Calibration for Uncertainty Quantification of Energy Time Series over Graphs
Keivan Faghih Niresi, Alice Cicirello, Olga Fink
Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems. Spatial-temporal graph neural networks (STGNNs) have r…
cs.LG2026
Virtual Smart Metering in District Heating Networks via Heterogeneous Spatial-Temporal Graph Neural Networks
Keivan Faghih Niresi, Christian Møller Jensen, Carsten Skovmose Kallesøe +2
Intelligent operation of thermal energy networks aims to improve energy efficiency, reliability, and operational flexibility through data-driven control, predictive optimization, a…