用相位建模时间序列周期性，参数少千级却更高效准确。
PhaseFormer: From Patches to Phases for Efficient and Effective Time Series Forecasting
- 从补丁转为相位建模，用紧凑相位嵌入实现高效预测
- 仅约1000参数即达顶尖性能，复杂数据上优势明显
- 适合追求轻量化与高精度的时序预测场景
周期性是时间序列的核心特征，长期以来在预测中起关键作用。近年深度学习方法通过将片段视为基本单元来增强周期性建模，但其效率受限于庞大的参数量和高昂的计算成本。本文首次明确揭示了基于片段处理的本质低效性，并提供了真实数据的有力证据。为此，我们提出相位视角建模周期性，设计出高效且有效的PhaseFormer。该模型通过紧凑的相位嵌入实现逐相位预测，并利用轻量路由机制实现高效的跨相位交互。大量实验表明，PhaseFormer仅需约1000个参数，即可在多个基准数据集上持续达到领先性能。尤其在大规模、复杂数据集上表现卓越，而同类高效模型常在此类任务中表现不佳。本工作标志着迈向真正高效且精准的时间序列预测的重要一步。代码已开源：https://github.com/neumyor/PhaseFormer_TSL
原文摘要 · Abstract (English)
Periodicity is a fundamental characteristic of time series data and has long played a central role in forecasting. Recent deep learning methods strengthen the exploitation of periodicity by treating patches as basic tokens, thereby improving predictive effectiveness. However, their efficiency remains a bottleneck due to large parameter counts and heavy computational costs. This paper provides, for the first time, a clear explanation of why patch-level processing is inherently inefficient, supported by strong evidence from real-world data. To address these limitations, we introduce a phase perspective for modeling periodicity and present an efficient yet effective solution, PhaseFormer. PhaseFormer features phase-wise prediction through compact phase embeddings and efficient cross-phase interaction enabled by a lightweight routing mechanism. Extensive experiments demonstrate that PhaseFormer achieves state-of-the-art performance with around 1k parameters, consistently across benchmark datasets. Notably, it excels on large-scale and complex datasets, where models with comparable efficiency often struggle. This work marks a significant step toward truly efficient and effective time series forecasting. Code is available at this repository: https://github.com/neumyor/PhaseFormer_TSL
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