Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting

H Liu, Z Dong, R Jiang, J Deng, J Deng… - Proceedings of the …, 2023 - dl.acm.org
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic
forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the …

Spatial-temporal large language model for traffic prediction

C Liu, S Yang, Q Xu, Z Li, C Long, Z Li… - arXiv preprint arXiv …, 2024 - arxiv.org
Traffic prediction, a critical component for intelligent transportation systems, endeavors to
foresee future traffic at specific locations using historical data. Although existing traffic …

[HTML][HTML] RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction

Y Liu, S Rasouli, M Wong, T Feng, T Huang - Information Fusion, 2024 - Elsevier
Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart
cities. Travelers as well as urban managers rely on reliable traffic information to make their …

Exploring progress in multivariate time series forecasting: Comprehensive benchmarking and heterogeneity analysis

Z Shao, F Wang, Y Xu, W Wei, C Yu, Z Zhang… - arXiv preprint arXiv …, 2023 - arxiv.org
Multivariate Time Series (MTS) widely exists in real-word complex systems, such as traffic
and energy systems, making their forecasting crucial for understanding and influencing …

Multispans: A multi-range spatial-temporal transformer network for traffic forecast via structural entropy optimization

D Zou, S Wang, X Li, H Peng, Y Wang, C Liu… - Proceedings of the 17th …, 2024 - dl.acm.org
Traffic forecasting is a complex multivariate time-series regression task of paramount
importance for traffic management and planning. However, existing approaches often …

WHEN: A Wavelet-DTW hybrid attention network for heterogeneous time series analysis

J Wang, C Yang, X Jiang, J Wu - Proceedings of the 29th ACM SIGKDD …, 2023 - dl.acm.org
Given its broad applications, time series analysis has gained substantial research attention
but remains a very challenging task. Recent years have witnessed the great success of deep …

Stg-mamba: Spatial-temporal graph learning via selective state space model

L Li, H Wang, W Zhang, A Coster - arXiv preprint arXiv:2403.12418, 2024 - arxiv.org
Spatial-Temporal Graph (STG) data is characterized as dynamic, heterogenous, and non-
stationary, leading to the continuous challenge of spatial-temporal graph learning. In the …

Cool: a conjoint perspective on spatio-temporal graph neural network for traffic forecasting

W Ju, Y Zhao, Y Qin, S Yi, J Yuan, Z Xiao, X Luo… - Information …, 2024 - Elsevier
This paper investigates traffic forecasting, which attempts to forecast the future state of traffic
based on historical situations. This problem has received ever-increasing attention in …

Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective

B Wang, P Wang, Y Zhang, X Wang, Z Zhou… - Proceedings of the …, 2024 - ojs.aaai.org
With the progress of urban transportation systems, a significant amount of high-quality traffic
data is continuously collected through streaming manners, which has propelled the …

A survey on diffusion models for time series and spatio-temporal data

Y Yang, M Jin, H Wen, C Zhang, Y Liang, L Ma… - arXiv preprint arXiv …, 2024 - arxiv.org
The study of time series data is crucial for understanding trends and anomalies over time,
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …