9 papers
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…
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…
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…
Graph Signal Separation with Learnable Spectral Filters
Keivan Faghih Niresi, Dorina Thanou, Olga Fink
Separating multiple graph signals from a single observed mixture is an inherently ill-posed problem that traditionally relies on restrictive and handcrafted priors. This letter add…
Time-Vertex Machine Learning for Optimal Sensor Placement in Temporal Graph Signals: Applications in Structural Health Monitoring
Keivan Faghih Niresi, Jun Qing, Mengjie Zhao +1
Structural Health Monitoring (SHM) plays a crucial role in maintaining the safety and resilience of infrastructure. As sensor networks grow in scale and complexity, identifying the…
RINS-T: Robust Implicit Neural Solvers for Time Series Linear Inverse Problems
Keivan Faghih Niresi, Zepeng Zhang, Olga Fink
Time series data are often affected by various forms of corruption, such as missing values, noise, and outliers, which pose significant challenges for tasks such as forecasting and…
From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM
Olga Fink, Ismail Nejjar, Vinay Sharma +13
Prognostics and Health Management ensures the reliability, safety, and efficiency of complex engineered systems by enabling fault detection, anticipating equipment failures, and op…
Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation
Keivan Faghih Niresi, Lucas Kuhn, Gaëtan Frusque +1
Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby fa…
Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems
Keivan Faghih Niresi, Mengjie Zhao, Hugo Bissig +2
The use of Internet of Things (IoT) sensors for air pollution monitoring has significantly increased, resulting in the deployment of low-cost sensors. Despite this advancement, acc…