TiWeaver：通过上下文补丁实现统一的时间动态建模
TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching
AI总结：
针对多变量时间序列中因缺失值和非均匀采样等不规则性导致的动态复杂性和通道间异步依赖问题，提出TiWeaver框架，通过图引导自适应分词器（G²AT）和细粒度异步依赖提取器（FADE）实现自适应建模，在12个数据集上取得最高25%的性能提升。
摘要：
多变量时间序列预测在现实世界应用中扮演着关键角色，包括天气预报、股票分析和健康监测。由于数据源的多样性，时间序列表现出多样的时间动态，通常伴随着各种不规则性，如缺失值和非均匀采样频率。这些不规则性导致跨通道的复杂异步时间依赖。因此，具有固定补丁方案的单一模型往往难以很好地适应多样化的多变量时间序列，阻碍了准确预测。在本文中，我们提出了TiWeaver，一个统一框架，旨在自适应地处理时间动态和细粒度的通道间依赖。具体来说，我们引入了一个图引导自适应分词器（G²AT），通过联合考虑时间密度和表示一致性，将时间序列划分为高度上下文连贯的补丁。此外，我们提出了一个细粒度异步依赖提取器（FADE），旨在建模细粒度的异步通道间依赖，同时结合长期历史依赖。我们在12个真实世界时间序列数据集上评估了TiWeaver，它取得了最先进的性能，优于现有方法高达25%。这些结果证明了其在多样化领域和数据特征上的鲁棒性和有效性。
英文摘要：
Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitoring. Due to the diversity of data sources, time series exhibit diverse temporal dynamics, often accompanied by various irregularities such as missing values and non-uniform sampling frequencies. Such irregularities lead to complex and asynchronous temporal dependencies across channels. Thus, a single model with a fixed patching scheme often fails to adapt well to diverse multivariate time series, hindering accurate forecasting. In this paper, we propose TiWeaver, a unified framework designed to handle temporal dynamics and fine-grained inter-channel dependencies adaptively. Specifically, we introduce a Graph-Guided Adaptive Tokenizer (G$^2$AT) that divides time series into high contextually coherent patches by jointly considering temporal density and representation consistency. In addition, we propose a Fine-grained Asynchronous Dependency Extractor (FADE), which is designed to model fine-grained asynchronous inter-channel dependencies while incorporating long-term historical dependencies. We evaluate TiWeaver on 12 real-world time series datasets, where it achieves state-of-the-art performance, outperforming existing methods up to 25%. These results demonstrate its robustness and effectiveness across diverse domains and data characteristics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
