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
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
