SageFormer：面向长期多变量时间序列预测的序列感知框架
SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting
AI总结：
本文提出序列感知框架SageFormer，一种图增强Transformer模型，用于长期多变量时间序列预测，通过显式建模序列间依赖并削减冗余信息，在真实与合成数据集上超越现有最先进方法。
摘要：
在蓬勃发展的物联网生态系统中，多变量时间序列（MTS）数据已变得无处不在，凸显了时间序列预测在众多应用中的基础性作用。长期MTS预测的关键挑战要求模型能够熟练捕捉序列内和序列间的依赖关系。深度学习的近期进展，尤其是Transformer，已展现出潜力。然而，许多主流方法要么边缘化序列间依赖，要么完全忽视它们。为弥补这一差距，本文引入了一种新颖的序列感知框架，明确旨在强调此类依赖的重要性。该框架的核心是我们的具体实现：SageFormer。作为一种序列感知的图增强Transformer模型，SageFormer利用图结构熟练地识别并建模序列间的复杂关系。除了捕捉多样化的时间模式外，它还削减了跨序列的冗余信息。值得注意的是，序列感知框架能与现有基于Transformer的模型无缝集成，增强它们理解序列间关系的能力。在真实世界和合成数据集上的大量实验验证了SageFormer相较于当代最先进方法的优越性能。
英文摘要：
In the burgeoning ecosystem of Internet of Things, multivariate time series (MTS) data has become ubiquitous, highlighting the fundamental role of time series forecasting across numerous applications. The crucial challenge of long-term MTS forecasting requires adept models capable of capturing both intra- and inter-series dependencies. Recent advancements in deep learning, notably Transformers, have shown promise. However, many prevailing methods either marginalize inter-series dependencies or overlook them entirely. To bridge this gap, this paper introduces a novel series-aware framework, explicitly designed to emphasize the significance of such dependencies. At the heart of this framework lies our specific implementation: the SageFormer. As a Series-aware Graph-enhanced Transformer model, SageFormer proficiently discerns and models the intricate relationships between series using graph structures. Beyond capturing diverse temporal patterns, it also curtails redundant information across series. Notably, the series-aware framework seamlessly integrates with existing Transformer-based models, enriching their ability to comprehend inter-series relationships. Extensive experiments on real-world and synthetic datasets validate the superior performance of SageFormer against contemporary state-of-the-art approaches.
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。
