3 papers
cs.LG2026
PPDL: A Real-world Industrial User Retention Ratio Forecasting Framework Integrating Physical Priors with Deep Learning
Zibo Zhao, Zhengxiong Guan, Chaoli Zhang +4
In multi-channel paid user acquisition, early and accurate prediction of user retention at the channel level is crucial for optimizing budget allocation. User retention curves disp…
cs.LG2026
Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariates
Linxiao Yang, Xue Jiang, Gezheng Xu +9
Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted featur…
cs.LG2025
SolarBoost: Distributed Photovoltaic Power Forecasting Amid Time-varying Grid Capacity
Linyuan Geng, Linxiao Yang, Xinyue Gu +1
This paper presents SolarBoost, a novel approach for forecasting power output in distributed photovoltaic (DPV) systems. While existing centralized photovoltaic (CPV) methods are a…