17 citations · 24 across the 7 of their papers we have counts for
8 papers · 1 filter
DynG-Diff: A State-Aware Dynamic Guidance Diffusion Framework for Probabilistic Time Series Forecasting
Zhente Zhang, Zhengwei Ni, Wei Fan
Probabilistic multivariate time series (MTS) forecasting is crucial for modeling complex dynamical systems. However, existing diffusion-based methods rely on task-specific conditio…
Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives
Sixun Dong, Wei Fan, Teresa Wu +1
Time series forecasting traditionally relies on unimodal numerical inputs, which often struggle to capture high-level semantic patterns due to their dense and unstructured nature.…
Dual-stage Flows-based Generative Modeling for Traceable Urban Planning
Xuanming Hu, Wei Fan, Dongjie Wang +3
Urban planning, which aims to design feasible land-use configurations for target areas, has become increasingly essential due to the high-speed urbanization process in the modern e…
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Wei Fan, Kunpeng Liu, Hao Liu +3
Feature selection and instance selection are two important techniques of data processing. However, such selections have mostly been studied separately, while existing work towards…
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
Wei Fan, Shun Zheng, Xiaohan Yi +4
Periodic time series (PTS) forecasting plays a crucial role in a variety of industries to foster critical tasks, such as early warning, pre-planning, resource scheduling, etc. Howe…
Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop
Wei Fan, Kunpeng Liu, Hao Liu +3
We study the problem of balancing effectiveness and efficiency in automated feature selection. After exploring many feature selection methods, we observe a computational dilemma: 1…