发表机构
Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CAMP是一种周期感知多尺度补丁混合器，通过自适应周期学习、horizon引导补丁混合及多分辨率建模，在七个长期预测基准和四个PEMS交通基准的十六个设置中取得优异预测性能。
AI 中文摘要
现实世界的时间序列通常由重复模式支配，但其主导周期可能在不同数据集、预测设置和单个输入窗口间存在差异。现有的周期感知预测器通常依赖于在数据集层面选择的单一周期，当周期行为随时间变化或存在多个周期共存时，这种方式会受到限制。此外，基于补丁的模型通常对所有补丁位置进行统一处理，尽管离预测边界较远的补丁可能需要更广泛的上下文细化，而近期补丁包含的信息应更直接地保留。在去除周期行为后，剩余的动态可能也跨越多个时间分辨率，无法在单一尺度上得到充分描述。我们引入CAMP，一种周期感知多尺度补丁混合器，旨在应对这些挑战。自适应周期学习模块为每个输入窗口单独识别主导频率，生成历史和未来周期分量，无需预定义的周期长度。 horizon引导补丁混合器引入位置相关的细化，允许早期补丁融入更广泛的时间上下文，同时保留靠近预测边界的信息。CAMP进一步通过时间对齐的多分辨率表示对去周期化残差进行建模，使不同尺度的互补动态能在一个预测框架内被捕获。在七个长期预测基准上，CAMP在六个数据集上取得最佳平均MSE，在六个数据集上取得最佳或并列最佳MAE；在四个PEMS交通基准的十六个设置中，其MSE获胜次数最多。
英文摘要
Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individual input windows. Existing cycle-aware forecasters commonly rely on a single period selected at the dataset level, which can be restrictive when periodic behavior changes over time or when multiple cycles coexist. Moreover, patch-based models typically process all patch positions uni- formly, although patches farther from the forecast boundary may require broader contextual refinement, while recent patches contain information that should be preserved more directly. Af- ter cyclic behavior is removed, the remaining dynamics may also span multiple temporal resolutions and cannot be adequately de- scribed at a single scale. We introduce CAMP, a Cycle-Aware Multi-Scale Patch Mixer designed to address these challenges. The Adaptive Cycle Learning module identifies dominant fre- quencies separately for each input window and generates both historical and future cyclic components without requiring a pre- defined cycle length. The Horizon-Guided Patch Mixer intro- duces position-dependent refinement, allowing earlier patches to incorporate broader temporal context while preserving infor- mation close to the forecast boundary. CAMP further models the de-cycled residual through temporally aligned multi-resolution representations, enabling complementary dynamics at different scales to be captured within one forecasting framework. Across seven long-term forecasting benchmarks, CAMP achieves the best average MSE on six datasets and the best or tied-best MAE on six. It also obtains the highest MSE win count across sixteen settings on four PEMS traffic benchmarks.
