6 citations · 14 across the 4 of their papers we have counts for
5 papers
TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning
Jiaxin Gao, Wenbo Hu, Dongxiao Zhang +1
Electrical energy is essential in today's society. Accurate electrical load forecasting is beneficial for better scheduling of electricity generation and saving electrical energy.…
Accurate and Reliable Forecasting using Stochastic Differential Equations
Peng Cui, Zhijie Deng, Wenbo Hu +1
It is critical yet challenging for deep learning models to properly characterize uncertainty that is pervasive in real-world environments. Although a lot of efforts have been made,…
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting
Qingyi Pan, Wenbo Hu, Jun Zhu
Time series forecasting is an important yet challenging task. Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to impro…
Dynamic Window-level Granger Causality of Multi-channel Time Series
Zhiheng Zhang, Wenbo Hu, Tian Tian +1
Granger causality method analyzes the time series causalities without building a complex causality graph. However, the traditional Granger causality method assumes that the causali…
Calibrated Reliable Regression using Maximum Mean Discrepancy
Peng Cui, Wenbo Hu, Jun Zhu
Accurate quantification of uncertainty is crucial for real-world applications of machine learning. However, modern deep neural networks still produce unreliable predictive uncertai…