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Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting
Jinhao Li, Hao Wang
Accurate electricity load forecasting is a crucial prerequisite for stable power system operations. While prevalent deep learning models present competitive performance, they often…
ImProNCDE: Impulse-Corrected Neural Controlled Differential Equations with Prototype Learning for Longitudinal Prognosis Prediction
Hao Wang, Yupeng Xu, Jinghao Lin +5
Longitudinal ophthalmic imaging analysis is an essential step for prognosis prediction in ophthalmic diseases. However, AI-assisted prognosis models are challenged by follow-up seq…
LGNO: A Local-Global Neural Operator for Hyperbolic Conservation Laws
Hao Wang, Chi-Wang Shu, Qi Tang
Solutions of hyperbolic conservation laws exhibit both smooth structures across large scales and sharp localized features such as shocks and contact discontinuities, making them di…
Spatio-Temporal Wildfire Spread Prediction in Canada using a Video Swin-Hybrid-U-Net and Satellite Imagery
Maulik Srivastava, Esha Saha, Hao Wang
Background: Wildfires in Canada present increasing threats to ecosystems, communities, and infrastructure, demanding accurate forecasting tools to aid mitigation efforts. Existing…
Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecasting
Yan Leng, Thibaut Mastrolia, Hao Wang
Time series driven by unobserved latent states frequently exhibit abrupt jump discontinuities whose timing and magnitude cannot be predicted from observed history alone. Classical…
Turning mechanistic models into forecasters by using machine learning
Amit K. Chakraborty, Hao Wang, Pouria Ramazi
The equations of complex dynamical systems may not be identified by expert knowledge, especially if the underlying mechanisms are unknown. Data-driven discovery methods address thi…
PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data across Nodes
Yiming Zhou, Jiahao Wang, Mingyue Cheng +3
While collaborative forecasting on distributed time series is highly desirable, directly pooling localized datasets is often impractical due to data sharing constraints. Federated…