提出轻量模块APT，提升时间序列在分布偏移下的预测精度。
APT: Affine Prototype-Timestamp For Time Series Forecasting Under Distribution Shift
- 用时间戳引导的原型学习动态生成仿射参数
- 在6个数据集上显著提升分布偏移下的预测效果
- 兼容任意模型且计算开销极小，适合工业场景
分布偏移下的时间序列预测仍具挑战性，现有深度学习模型常依赖局部统计归一化（如均值和方差），难以捕捉全局分布变化。尽管RevIN等方法尝试分离分布与模式，但仍面临缺失值、噪声观测及通道级仿射变换失效的问题。为此，我们提出仿射原型时间戳（APT）模块，一种轻量且灵活的即插即用组件，可将全局分布特征注入归一化-预测流程。通过时间戳条件下的原型学习，APT动态生成调节输入与输出序列的仿射参数，使主干网络能够从自监督、分布感知的聚类实例中学习。APT兼容任意预测主干与归一化策略，仅引入极小计算开销。在六个基准数据集及多种主干-归一化组合上的大量实验表明，APT在分布偏移下显著提升了预测性能。
原文摘要 · Abstract (English)
Time series forecasting under distribution shift remains challenging, as existing deep learning models often rely on local statistical normalization (e.g., mean and variance) that fails to capture global distribution shift. Methods like RevIN and its variants attempt to decouple distribution and pattern but still struggle with missing values, noisy observations, and invalid channel-wise affine transformation. To address these limitations, we propose Affine Prototype Timestamp (APT), a lightweight and flexible plug-in module that injects global distribution features into the normalization-forecasting pipeline. By leveraging timestamp conditioned prototype learning, APT dynamically generates affine parameters that modulate both input and output series, enabling the backbone to learn from self-supervised, distribution-aware clustered instances. APT is compatible with arbitrary forecasting backbones and normalization strategies while introducing minimal computational overhead. Extensive experiments across six benchmark datasets and multiple backbone-normalization combinations demonstrate that APT significantly improves forecasting performance under distribution shift.
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