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.

翻译：在物联网蓬勃发展的生态系统中，多变量时间序列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。长期多变量时间序列预测的关键挑战要求模型能够同时捕获序列内和序列间的依赖关系。深度学习的最新进展，特别是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关系，要么完全忽略它们。为弥合这一差距，本文提出了一种新颖的序列感知框架，明确设计以强调此类依赖关系的重要性。该框架的核心是我们的具体实现：SageFormer。作为一种序列感知图增强Transformer模型，SageFormer能够熟练地利用图结构识别并建模序列之间错综复杂的关系。在捕获多样化的时间模式之外，它还能缩减跨序列的冗余信息。值得注意的是，该序列感知框架可与现有的基于Transformer的模型无缝集成，增强其理解序列间关系的能力。在真实世界与合成数据集上的广泛实验验证了SageFormer相较于当代最先进方法的优越性能。