6 citations · 10 across the 14 of their papers we have counts for
14 papers
AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks
Zhongyue Zhang, Guangyin Jin, Yuxuan Liang +2
Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs). Traditional methods rely on distance-based kernels with predefined para…
Discrete Prototypical Memories for Federated Time Series Foundation Models
Liwei Deng, Qingxiang Liu, Xinhe Niu +5
Leveraging Large Language Models (LLMs) as federated learning (FL)-based time series foundation models offers a promising way to transfer the generalization capabilities of LLMs to…
Aeolus: A Multi-structural Flight Delay Dataset
Lin Xu, Xinyun Yuan, Yuxuan Liang +2
We introduce Aeolus, a large-scale Multi-modal Flight Delay Dataset designed to advance research on flight delay prediction and support the development of foundation models for tab…
GeoGR^2:Zero-Shot Geospatial Inference via Geostatistically-Guided Iterative Refinement with LLMs
Jinfan Tang, Kunming Wu, Xieruifeng Gong +4
Standard large language model prompting treats geospatial inference as independent, instance-wise prediction, ignoring the fundamental spatial dependencies that govern geographic r…
Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers
Yanchen Zhu, Honghui Zou, Chufan Liu +3
The success of vehicle electrification relies on efficient and adaptable charging infrastructure. Fixed-location charging stations often suffer from underutilization or congestion…
Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning
Jia Liu, Cheng Jinguo, Xia Fang +2
Time series foundation models excel at diverse time series forecasting tasks, but their capacity for continuous improvement through incremental learning remains unexplored. We pres…
AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks
Qiongyan Wang, Yutong Xia, Siru ZHong +6
Monitoring real-time air quality is essential for safeguarding public health and fostering social progress. However, the widespread deployment of air quality monitoring stations is…
ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks
Wanlin Cai, Kun Wang, Hao Wu +2
The challenge of effectively learning inter-series correlations for multivariate time series forecasting remains a substantial and unresolved problem. Traditional deep learning mod…
NuwaTS: a Foundation Model Mending Every Incomplete Time Series
Jinguo Cheng, Chunwei Yang, Wanlin Cai +3
Time series imputation is critical for many real-world applications and has been widely studied. However, existing models often require specialized designs tailored to specific mis…
Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts
Kun Wang, Hao Wu, Guibin Zhang +5
In this paper, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames.…
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
Wanlin Cai, Yuxuan Liang, Xianggen Liu +2
Multivariate time series forecasting poses an ongoing challenge across various disciplines. Time series data often exhibit diverse intra-series and inter-series correlations, contr…
Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach
Jinguo Cheng, Ke Li, Yuxuan Liang +3
Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Conventionally, urban mobility data has been structured as sp…
TimeSQL: Improving Multivariate Time Series Forecasting with Multi-Scale Patching and Smooth Quadratic Loss
Site Mo, Haoxin Wang, Bixiong Li +3
Time series is a special type of sequence data, a sequence of real-valued random variables collected at even intervals of time. The real-world multivariate time series comes with n…
BreakingBED -- Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks
Manoj Rohit Vemparala, Alexander Frickenstein, Nael Fasfous +8
Deploying convolutional neural networks (CNNs) for embedded applications presents many challenges in balancing resource-efficiency and task-related accuracy. These two aspects have…