Multivariate time series forecasting is crucial for various applications, such as financial investment, energy management, weather forecasting, and traffic optimization. However, accurate forecasting is challenging due to two main factors. First, real-world time series often show heterogeneous temporal patterns caused by distribution shifts over time. Second, correlations among channels are complex and intertwined, making it hard to model the interactions among channels precisely and flexibly. In this study, we address these challenges by proposing a general framework called DUET, which introduces dual clustering on the temporal and channel dimensions to enhance multivariate time series forecasting. First, we design a Temporal Clustering Module (TCM) that clusters time series into fine-grained distributions to handle heterogeneous temporal patterns. For different distribution clusters, we design various pattern extractors to capture their intrinsic temporal patterns, thus modeling the heterogeneity. Second, we introduce a novel Channel-Soft-Clustering strategy and design a Channel Clustering Module (CCM), which captures the relationships among channels in the frequency domain through metric learning and applies sparsification to mitigate the adverse effects of noisy channels. Finally, DUET combines TCM and CCM to incorporate both the temporal and channel dimensions. Extensive experiments on 25 real-world datasets from 10 application domains, demonstrate the state-of-the-art performance of DUET.

翻译：多元时间序列预测在金融投资、能源管理、气象预报和交通优化等诸多应用中至关重要。然而，准确的预测面临两大主要挑战：首先，现实世界中的时间序列常因随时间推移的分布漂移而呈现异质性时序模式；其次，各通道间的相关性复杂且相互交织，难以精确而灵活地建模通道间的交互作用。本研究提出名为DUET的通用框架以应对这些挑战，该框架通过在时序维度与通道维度引入双重聚类来增强多元时间序列预测。首先，我们设计了时序聚类模块，将时间序列聚类为细粒度分布以处理异质性时序模式。针对不同分布簇，我们设计了多种模式提取器以捕捉其本质时序模式，从而实现对异质性的建模。其次，我们提出新颖的通道软聚类策略，设计了通道聚类模块，通过度量学习在频域捕获通道间关系，并应用稀疏化以减轻噪声通道的负面影响。最终，DUET融合时序聚类模块与通道聚类模块，同时纳入时序与通道维度信息。在涵盖10个应用领域的25个真实数据集上的大量实验表明，DUET取得了最先进的预测性能。