A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
[HTML][HTML] Graph spatiotemporal process for multivariate time series anomaly detection with missing values
The detection of anomalies in multivariate time series data is crucial for various practical
applications, including smart power grids, traffic flow forecasting, and industrial process …
applications, including smart power grids, traffic flow forecasting, and industrial process …
Dyedgegat: Dynamic edge via graph attention for early fault detection in iiot systems
In the Industrial Internet of Things (IIoT), condition monitoring sensor signals from complex
systems often exhibit nonlinear and stochastic spatial-temporal dynamics under varying …
systems often exhibit nonlinear and stochastic spatial-temporal dynamics under varying …
Enhancing urban flow prediction via mutual reinforcement with multi-scale regional information
X Zhang, M Cao, Y Gong, X Wu, X Dong, Y Guo… - Neural Networks, 2025 - Elsevier
Abstract Intelligent Transportation Systems (ITS) are essential for modern urban
development, with urban flow prediction being a key component. Accurate flow prediction …
development, with urban flow prediction being a key component. Accurate flow prediction …
AD-NEv: A Scalable Multilevel Neuroevolution Framework for Multivariate Anomaly Detection
Anomaly detection tools and methods present a key capability in modern cyberphysical and
failure prediction systems. Despite the fast-paced development in deep learning …
failure prediction systems. Despite the fast-paced development in deep learning …
Vehicular Social Dynamic Anomaly Detection With Recurrent Mulit-Mask Aggregator Enabled VAE
Z Hu, Y He, Y Shen, M Jo, M Collotta… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Vehicle driving behavior analysis and detection tasks have become an indispensable part of
intelligent transportation systems. Accurate pattern recognition of potential anomalies during …
intelligent transportation systems. Accurate pattern recognition of potential anomalies during …
Rectifying inaccurate unsupervised learning for robust time series anomaly detection
Unsupervised time series anomaly detection is a challenging task. Data contamination
brings more challenges for the existing methods that rely on completely clean training data …
brings more challenges for the existing methods that rely on completely clean training data …
Multivariate Time Series Anomaly Detection Based on Dynamic Graph Neural Networks and Self-Distillation in Industrial Internet of Things
M Zhao, H Peng, L Li - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
Time series anomaly detection is critical to securing the Industrial Internet of Things (IIoT).
Although numerous deep learning-based methods have been proposed, these methods fail …
Although numerous deep learning-based methods have been proposed, these methods fail …
A Multitask Dynamic Graph Attention Autoencoder for Imbalanced Multilabel Time Series Classification
Graph learning is widely applied to process various complex data structures (eg, time series)
in different domains. Due to multidimensional observations and the requirement for accurate …
in different domains. Due to multidimensional observations and the requirement for accurate …
HFTCRNet: Hierarchical Fusion Transformer for Interbank Credit Rating and Risk Assessment
As a prominent application of deep neural networks in financial literature, bank credit ratings
play a pivotal role in safeguarding global economic stability and preventing crises. In the …
play a pivotal role in safeguarding global economic stability and preventing crises. In the …