1 citations · 2 across the 11 of their papers we have counts for
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Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting
Wentao Gao, Jiuyong Li, Lin Liu +4
Large \emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains. However, their application to regional climate fore…
Residual-Guided Multi-Resolution Refinement of Foundation Models: A Case Study in Drought Forecasting
Wentao Gao, Jiuyong Li, Lin Liu +4
Regional climate prediction presents unique challenges for time series foundation models, which typically process temporal patterns through single-pass inference. Expert climatolog…
UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning
Danhui Zhang, Zhe Wang, Qing Qing +6
Graph learning research has increasingly shifted toward continual graph learning (CGL), which better reflects real-world scenarios where graphs evolve over time. However, existing…
From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction
Wentao Gao, Jiuyong Li, Lin Liu +6
Zero-inflated data pose significant challenges in precipitation forecasting due to the predominance of zeros with sparse non-zero events. To address this, we propose the Zero Infla…
Deconfounded Time Series Forecasting: A Causal Inference Approach
Wentao Gao, Xiaojing Du, Wenjun Yu +3
Time series forecasting is a critical task in various domains, where accurate predictions can drive informed decision-making. Traditional forecasting methods often rely on current…
TSI: A Multi-View Representation Learning Approach for Time Series Forecasting
Wentao Gao, Ziqi Xu, Jiuyong Li +6
As the growing demand for long sequence time-series forecasting in real-world applications, such as electricity consumption planning, the significance of time series forecasting be…