Time series forecasting serves as an essential tool for many real-world applications, supporting tasks such as resource optimization and decision-making. Despite significant architectural advancements, most modern models still treat forecasting task as a fixed mapping from history to target horizons. This induces temporal decoupling across future time points and limits the model's ability to adapt to the evolving context as forecasting progresses. In this work, we present LeapTS, a novel framework that reformulates time series forecasting as a dynamic scheduling process over the prediction horizon. Specifically, LeapTS organizes the forecasting process into multi-level decisions using: (1) the hierarchical controller to dynamically select the optimal prediction scale and advancement length at each step, and (2) continuous-time state evolution driven by neural controlled differential equations. Within this process, the controlled update mechanism explicitly couples the irregular temporal dynamics with discrete scheduling feedback. Extensive evaluations on both real-world and synthetic datasets demonstrate that LeapTS improves overall forecasting performance by at least 7.4% while achieving a 2.6$\times$ to 5.3$\times$ inference speedup over representative Transformer-based models. Furthermore, by explicitly tracing the scheduling trajectories, we reveal how the model autonomously adapts its forecasting behavior to capture non-stationary dynamics.

翻译：时间序列预测是许多实际应用的重要工具，支持资源优化和决策制定等任务。尽管模型架构取得了显著进步，但大多数现代模型仍将预测任务视为从历史到目标区间的固定映射。这导致了未来时间点之间的时间解耦，并限制了模型在预测推进过程中适应不断变化的上下文的能力。在这项工作中，我们提出了LeapTS，一个新颖的框架，它将时间序列预测重新构想为预测区间上的动态调度过程。具体而言，LeapTS通过以下方式将预测过程组织成多级决策：(1) 层次化控制器，用于在每个步骤动态选择最优的预测尺度和前进步长；以及 (2) 由神经受控微分方程驱动的连续时间状态演化。在此过程中，受控更新机制显式地将不规则的时间动态与离散的调度反馈耦合在一起。在真实世界和合成数据集上的广泛评估表明，与具有代表性的基于Transformer的模型相比，LeapTS将整体预测性能提升了至少7.4%，同时实现了2.6倍到5.3倍的推理速度提升。此外，通过显式追踪调度轨迹，我们揭示了模型如何自主调整其预测行为以捕捉非平稳动态。