Graph neural networks for multivariate time series regression with application to seismic data
Abstract Machine learning, with its advances in deep learning has shown great potential in
analyzing time series. In many scenarios, however, additional information that can …
analyzing time series. In many scenarios, however, additional information that can …
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) …
Pay attention to evolution: Time series forecasting with deep graph-evolution learning
Time-series forecasting is one of the most active research topics in artificial intelligence. It
has the power to bring light to problems in several areas of knowledge, such as …
has the power to bring light to problems in several areas of knowledge, such as …
Discrete graph structure learning for forecasting multiple time series
Time series forecasting is an extensively studied subject in statistics, economics, and
computer science. Exploration of the correlation and causation among the variables in a …
computer science. Exploration of the correlation and causation among the variables in a …
Sparse graph learning from spatiotemporal time series
Outstanding achievements of graph neural networks for spatiotemporal time series analysis
show that relational constraints introduce an effective inductive bias into neural forecasting …
show that relational constraints introduce an effective inductive bias into neural forecasting …
Dynamic spatio-temporal graph network with adaptive propagation mechanism for multivariate time series forecasting
ZL Li, J Yu, GW Zhang, LY Xu - Expert Systems with Applications, 2023 - Elsevier
Spatio-temporal prediction on multivariate time series has received tremendous attention for
extensive applications in the real world, where the dynamic unknown spatio-temporal …
extensive applications in the real world, where the dynamic unknown spatio-temporal …
Learning the evolutionary and multi-scale graph structure for multivariate time series forecasting
Recent studies have shown great promise in applying graph neural networks for multivariate
time series forecasting, where the interactions of time series are described as a graph …
time series forecasting, where the interactions of time series are described as a graph …
Scalable spatiotemporal graph neural networks
Neural forecasting of spatiotemporal time series drives both research and industrial
innovation in several relevant application domains. Graph neural networks (GNNs) are often …
innovation in several relevant application domains. Graph neural networks (GNNs) are often …
Graph-time convolutional neural networks: Architecture and theoretical analysis
Devising and analysing learning models for spatiotemporal network data is of importance for
tasks including forecasting, anomaly detection, and multi-agent coordination, among others …
tasks including forecasting, anomaly detection, and multi-agent coordination, among others …
Connecting the dots: Multivariate time series forecasting with graph neural networks
Modeling multivariate time series has long been a subject that has attracted researchers
from a diverse range of fields including economics, finance, and traffic. A basic assumption …
from a diverse range of fields including economics, finance, and traffic. A basic assumption …