Irregular multivariate time series forecasting is a challenging yet important problem in real-world applications, where observations are often irregularly sampled and asynchronously recorded across variables. Existing time series foundation models are mostly built on regularly sampled sequences, making them difficult t...
Lin-Feng Wang, Rui-Tong Zhang, Kai Zhao et al.· 0 citations
BITS, a standardized, reproducible, and extensible benchmark for advancing research on irregular time series forecasting, is proposed and it is found that method performance varies substantially across irregularity characteristics, with no single modeling strategy consistently dominating.
Kang-Jia Yan, Lin-Feng Wang, Tian Shen et al.· 0 citations
This analysis transfers reverse-time discretization errors to the forward corruption law and treats two numerical schemes within a common framework, which identifies a local error, accumulates it through the forward evolution, and inserts the result into a common KL decomposition.
This work presents DREAM (Developing Recommender Engine with Agentic Methods), an autonomous optimization control architecture that adds a perception-aware, orchestrable, and auditable policy layer atop existing pipelines without replacing them, supporting agentic meta-control as a viable paradigm for industrial recomm...
Bin Zhang, Bo-Wen Zheng, Chao Yi et al.· 0 citations
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