CATF：基于 Manager-Worker 架构的上下文感知时间序列预测框架
CATF: A Manager-Worker Framework for Context-Aware Time-Series Forecasting
论文提出 CATF 框架，Manager 硬路由把任务分给专职 Worker，200 组对比里 162 组赢过单模型，还胜过 Time-MoE 15/20。
arXiv 论文提出 CATF，用 Transformer Manager 在训练和推理阶段都做硬路由，把输入分配给单个 Worker，避免 Mixture-of-Experts 和集成学习的人工数据划分与软路由开销。在 5 个上下文丰富数据集、4 个预测时长、2 种归一化方案下，CATF 在 200 组对比中 162 组优于单独模型，最佳 CATF 增强模型在 20 个设置中 19 个超过最佳单独基线，并在 20 个设置中 15 个胜过零样本 Time-MoE 基础模型。共享单车数据案例显示 Manager 按日历信息路由，各 Worker 专注不同的周模式。
CATF: A Manager-Worker Framework for Context-Aware Time-Series Forecasting
We study context-aware time-series forecasting, in which a single target variable is predicted from its own history and a set of contextual variables, such as weather and calendar information. As the context becomes more diverse, a single model struggles to capture all of its regimes, while ensemble learning and Mixture-of-Experts approaches rely on manual data partitioning or soft routing, which increases the inference cost and hinders interpretability. We propose CATF, a Context-Aware Time-Series Forecasting framework built on a Manager-Worker architecture: a Transformer-based Manager routes each input to a single Worker based on the context, using hard routing during both training and inference. The Workers thus specialize without manual data partitioning, and any single-model forecaster can serve as a Worker. On five context-rich datasets, four horizons, and two normalization schemes, CATF improves over its standalone counterpart in 162 of 200 comparisons, the best CATF-enhanced model outperforms the best standalone baseline in 19 of 20 settings, and it outperforms the zero-shot Time-MoE foundation model in 15 of 20 settings. A case study on bike-sharing data shows that the Manager routes inputs by the calendar and that each Worker specializes in a different weekly pattern.