arXiv 2505.057382025-05-25cs.LGcs.AI

基于离线聚类的准确高效多元时间序列预测

Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering

State Key Laboratory of Complex & Critical Software Environment（复杂与关键软件环境国家重点实验室）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（未来区块链与隐私计算北京先进创新中心）；Beihang University（北京航空航天大学）；HKGAI（Hong Kong University of Science and Technology）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究 institute）；Southern University of Science and Technology（南方科技大学）；Department of Data Science（数据科学系）

Yiming Niu, Jinliang Deng, Lulu Zhang, Zimu Zhou, Yongxin Tong

备注：: Accepted to ICDE 2025

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AI总结：

针对多元时间序列预测中Transformer二次方计算复杂度的瓶颈，提出基于离线聚类提取原型的FOCUS方法，将在线长程依赖建模降为线性复杂度，在多个基准上达到最先进精度并大幅降低计算成本。

摘要：

准确且高效的多元时间序列（MTS）预测对于交通管理和天气预测等应用至关重要，这些应用依赖于捕捉长时间范围的时间依赖关系以及实体之间的交互。现有方法，尤其是基于Transformer架构的方法，计算所有时间步上的两两依赖关系，导致计算复杂度随输入长度呈二次方增长。为克服这些挑战，我们提出了FOCUS（使用片段进行离线聚类的预测器），一种新颖的MTS预测方法，通过利用离线聚类提取的原型来简化长时间范围依赖建模。这些原型封装了数据所代表的真实世界系统中的高层事件，概括了相似时间片段的关键特征。在在线阶段，FOCUS动态地将这些模式适配到当前输入，并捕捉输入片段与高层事件之间的依赖关系，从而实现既准确又高效的预测。通过在离线聚类阶段识别原型，FOCUS将在线阶段中建模长时间范围依赖的计算复杂度降低至线性扩展。在多个不同基准上的广泛实验表明，FOCUS在显著降低计算成本的同时实现了最先进的准确性。

英文摘要：

Accurate and efficient multivariate time series (MTS) forecasting is essential for applications such as traffic management and weather prediction, which depend on capturing long-range temporal dependencies and interactions between entities. Existing methods, particularly those based on Transformer architectures, compute pairwise dependencies across all time steps, leading to a computational complexity that scales quadratically with the length of the input. To overcome these challenges, we introduce the Forecaster with Offline Clustering Using Segments (FOCUS), a novel approach to MTS forecasting that simplifies long-range dependency modeling through the use of prototypes extracted via offline clustering. These prototypes encapsulate high-level events in the real-world system underlying the data, summarizing the key characteristics of similar time segments. In the online phase, FOCUS dynamically adapts these patterns to the current input and captures dependencies between the input segment and high-level events, enabling both accurate and efficient forecasting. By identifying prototypes during the offline clustering phase, FOCUS reduces the computational complexity of modeling long-range dependencies in the online phase to linear scaling. Extensive experiments across diverse benchmarks demonstrate that FOCUS achieves state-of-the-art accuracy while significantly reducing computational costs.

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。