Time series forecasting has witnessed significant progress with deep learning. While prevailing approaches enhance forecasting performance by modifying architectures or introducing novel enhancement strategies, they often fail to dynamically disentangle and leverage the complex, intertwined temporal patterns inherent in time series, thus resulting in the learning of static, averaged representations that lack context-aware capabilities. To address this, we propose the Dual-Prototype Adaptive Disentanglement framework (DPAD), a model-agnostic auxiliary method that equips forecasting models with the ability of pattern disentanglement and context-aware adaptation. Specifically, we construct a Dynamic Dual-Prototype bank (DDP), comprising a common pattern bank with strong temporal priors to capture prevailing trend or seasonal patterns, and a rare pattern bank dynamically memorizing critical yet infrequent events, and then an Dual-Path Context-aware routing (DPC) mechanism is proposed to enhance outputs with selectively retrieved context-specific pattern representations from the DDP. Additionally, we introduce a Disentanglement-Guided Loss (DGLoss) to ensure that each prototype bank specializes in its designated role while maintaining comprehensive coverage. Comprehensive experiments demonstrate that DPAD consistently improves forecasting performance and reliability of state-of-the-art models across diverse real-world benchmarks.

翻译：随着深度学习的发展，时间序列预测取得了显著进展。尽管主流方法通过修改架构或引入新颖的增强策略来提升预测性能，但它们往往未能动态解耦并利用时间序列中固有的复杂交织时序模式，从而导致学习到静态的、平均化的表征，缺乏上下文感知能力。为解决这一问题，我们提出了双原型自适应解耦框架（DPAD），这是一种与模型无关的辅助方法，能够为预测模型提供模式解耦和上下文自适应能力。具体而言，我们构建了一个动态双原型库（DDP），它包含一个具有强时序先验的通用模式库以捕捉主流趋势或季节性模式，以及一个动态记忆关键但偶发事件的稀有模式库。随后，我们提出了一种双路径上下文感知路由（DPC）机制，通过从DDP中有选择地检索上下文特定的模式表征来增强输出。此外，我们引入了解耦引导损失（DGLoss），以确保每个原型库专注于其指定角色，同时保持全面的覆盖范围。综合实验表明，DPAD在各种真实世界基准测试中，能够持续提升最先进模型的预测性能和可靠性。