DrafTS：面向时间序列建模的时间感知分解与残差校正
DrafTS: Time-Aware Decomposition with Residual Correction for Time Series Modeling
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
- Abel AI Lab（Abel人工智能实验室）
- Squirrel Ai Learning（松鼠Ai学习）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
DrafTS通过时间感知分解与残差校正，在保留动态的同时降噪，提升多种时间序列建模骨干的性能。
AI中文摘要：
真实世界的时间序列包含不断演化的底层动态，这些动态伴有不规则变化，缺乏稳定的时间模式，通常被称为噪声。现有方法通过滤波频率或抑制噪声观测来处理这种混合，但它们要么遗漏时间演化，要么有抑制有用动态的风险。我们提出DrafTS，一个模型无关的框架，旨在通过时间感知分解与残差校正（用于时间序列）在保留演化动态的同时减少噪声。DrafTS利用从瞬时幅度和频率导出的特征，指导将序列分解为主要成分，旨在捕捉底层动态。一个任务特定的骨干模型处理主要成分，而一个轻量级校正模块利用残差信息来校正骨干输出。在四个时间序列建模任务中，DrafTS提升了六个不同的骨干模型，展示了其有效性。代码见此https URL。
英文摘要：
Real-world time series contain evolving underlying dynamics with irregular variations that lack stable temporal patterns and are often referred to as noise. Existing methods address this mixture by filtering frequencies or suppressing noisy observations. They either miss temporal evolution or risk suppressing useful dynamics. We propose DrafTS, a model-agnostic framework that aims to reduce noise while preserving evolving dynamics through time-aware Decomposition with ResiduAl correction For Time Series. DrafTS uses features derived from instantaneous amplitude and frequency to guide decomposition into a primary component intended to capture underlying dynamics. A task-specific backbone models the primary component, while a lightweight correction module uses residual information to correct the backbone output. Across four time series modeling tasks, DrafTS improves six diverse backbones, demonstrating its effectiveness. Code is at https://github.com/Autumn61q/DrafTS
