12 papers
Do Time-Series QA Systems Read the Time Series? Evidence Use and Reasoning Reliability
Zhuomin Chen, Jingchao Ni, Xu Zheng +4
In recent years, time-series question answering (QA) systems have made significant progress. However, generating a correct answer does not show whether retaining the supplied numer…
BridgeCast: Bridging Ocean Wave Forecasts to Reanalysis via Flow Matching with Exogenous Variables
Siyu Gan, Dongsheng Luo, Kunxiaojia Yuan +2
Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systematic biases in physics-based models. Phys…
MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters
ChengAo Shen, Wenchao Yu, Fangyu Wu +6
Time series forecasting (TSF) is evolving toward multimodal and agentic settings, yet using foundation models remains uneconomical in resource-constrained scenarios, where compact,…
Towards A Unified Information Bottleneck Framework for Time Series Explanations
Xu Zheng, Zichuan Liu, Zhuomin Chen +7
Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior. {Existing…
Information Bottleneck Learning for Faithful Time Series Forecasting Explanations
Xu Zheng, Wei Cheng, Zhuomin Chen +3
As forecasts increasingly drive decisions in fields such as energy, transportation, and healthcare, understanding the historical data behind these predictions has become as crucial…
DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts
Guiquan Sun, Xikun Zhang, Jingchao Ni +1
Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting. Existing CGL approaches typically adopt a task-based formula…
SVTime: Small Time Series Forecasting Models Informed by "Physics" of Large Vision Model Forecasters
ChengAo Shen, Ziming Zhao, Hanghang Tong +4
Time series AI is crucial for analyzing dynamic web content, driving a surge of pre-trained large models known for their strong knowledge encoding and transfer capabilities across…
From Binary to Continuous: Stochastic Re-Weighting for Robust Graph Explanation
Zhuomin Chen, Jingchao Ni, Hojat Allah Salehi +2
Graph Neural Networks (GNNs) have achieved remarkable performance in a wide range of graph-related learning tasks. However, explaining their predictions remains a challenging probl…
Uncovering Insights of Compound Flooding with Data-Driven AI
Xu Zheng, Chaohao Lin, Sipeng Chen +7
Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention. Existing forecasting approache…
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
ChengAo Shen, Zhengzhang Chen, Dongsheng Luo +3
Causal discovery is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps. Traditional statistical causal discovery met…
Explanation-Preserving Augmentation for Semi-Supervised Graph Representation Learning
Zhuomin Chen, Jingchao Ni, Hojat Allah Salehi +4
Self-supervised graph representation learning (GRL) typically generates paired graph augmentations from each graph to infer similar representations for augmentations of the same gr…
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
Zhuomin Chen, Jiaxing Zhang, Jingchao Ni +6
Graph Neural Networks (GNNs) have become a building block in graph data processing, with wide applications in critical domains. The growing needs to deploy GNNs in high-stakes appl…