TAMP-S2GCNets: coupling time-aware multipersistence knowledge representation with spatio-supra graph convolutional networks for time-series forecasting
Y Chen, I Segovia-Dominguez… - International …, 2022 - openreview.net
Graph Neural Networks (GNNs) are proven to be a powerful machinery for learning complex
dependencies in multivariate spatio-temporal processes. However, most existing GNNs …
dependencies in multivariate spatio-temporal processes. However, most existing GNNs …
Time-conditioned dances with simplicial complexes: Zigzag filtration curve based supra-hodge convolution networks for time-series forecasting
Graph neural networks (GNNs) offer a new powerful alternative for multivariate time series
forecasting, demonstrating remarkable success in a variety of spatio-temporal applications …
forecasting, demonstrating remarkable success in a variety of spatio-temporal applications …
Z-GCNETs: Time zigzags at graph convolutional networks for time series forecasting
There recently has been a surge of interest in developing a new class of deep learning (DL)
architectures that integrate an explicit time dimension as a fundamental building block of …
architectures that integrate an explicit time dimension as a fundamental building block of …
Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
Multivariate Time-Series (MTS) data is crucial in various application fields. With its
sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits …
sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits …
St-unet: A spatio-temporal u-network for graph-structured time series modeling
The spatio-temporal graph learning is becoming an increasingly important object of graph
study. Many application domains involve highly dynamic graphs where temporal information …
study. Many application domains involve highly dynamic graphs where temporal information …
Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting
Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications.
Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly …
Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Long-range transformers for dynamic spatiotemporal forecasting
Multivariate time series forecasting focuses on predicting future values based on historical
context. State-of-the-art sequence-to-sequence models rely on neural attention between …
context. State-of-the-art sequence-to-sequence models rely on neural attention between …
Network of tensor time series
Co-evolving time series appears in a multitude of applications such as environmental
monitoring, financial analysis, and smart transportation. This paper aims to address the …
monitoring, financial analysis, and smart transportation. This paper aims to address the …
Graph neural networks for temporal graphs: State of the art, open challenges, and opportunities
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …
graph-structured data. However, many real-world systems are dynamic in nature, since the …
相关搜索
- convolution networks time series forecasting
- series forecasting graph neural network
- convolution networks filtration curve
- convolution networks simplicial complexes
- filtration curve time series forecasting
- simplicial complexes time series forecasting
- st unet time series
- graph convolutional networks time series
- temporal graphs neural networks