Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systematic biases in physics-based models. Physical models, while widely used, rely on approximations and parameterizations that limit their accuracy under complex ocean-atmosphere condit...
Si-Yu Gan, Dong-Sheng Luo, K. Yuan et al.· 0 citations
In recent years, time-series question answering (QA) systems have made significant progress. However, generating a correct answer does not show whether retaining the supplied numerical series improves task performance, nor whether the prediction is sensitive to changes in that input. While some systems provide rational...
Zhuo-Min Chen, Jing-Chao Ni, Xu Zheng et al.· 0 citations
A unified objective function for explainable time series learning that bridges attribution and counterfactual reasoning within a single framework is proposed and a novel explanation framework is introduced that learns a parametric transformation network to construct explanation-embedded instances.
Xu Zheng, Zichuan Liu, Zhuo-Min Chen et al.· 0 citations
IB-Forecast is proposed, an inherently interpretable multivariate time-series forecasting framework that decomposes forecasting into a learned periodic component and a residual component computed with explainable masks over input tokens that guarantees high explanation fidelity.
This work proposes MetaCaster, a meta-harness-optimized multi-agent framework that uses agentic data generation to automatically train specialized lightweight forecasters from only a few examples and textual contexts and highlights a new TSF paradigm in which agents act not as forecasters but as intermediary engineers...
Chengao Shen, Wen-Chao Yu, Fang-Yu Wu et al.· 0 citations
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