通过动态选择多样邻居与多尺度融合提升时序预测精度
Graph Neural Networks with Diversity-aware Neighbor Selection and Dynamic Multi-scale Fusion for Multivariate Time Series Forecasting
- 引入多样性感知邻居选择，避免信息冗余
- 动态融合多时间尺度预测结果，提升整体表现
- 在真实数据集上显著优于现有方法，适合时序建模研究
近年来，大量深度模型被提出以提升多变量时间序列（MTS）预测性能。其中，图神经网络（GNN）因其能显式建模变量间依赖关系而展现出巨大潜力。然而，现有方法常忽略邻居间信息的多样性，导致信息聚合冗余；且最终预测通常仅依赖单一时间尺度的表示。为此，本文提出多样性感知邻居选择与动态多尺度融合的图神经网络（DIMIGNN）。DIMIGNN引入多样性感知邻居选择机制（DNSM），确保每个变量与其邻居具有高信息相似性，同时保持邻居间的多样性。此外，设计动态多尺度融合模块（DMFM），动态调整不同时间尺度预测结果对最终输出的贡献。在真实世界数据集上的大量实验表明，DIMIGNN持续优于已有方法。
原文摘要 · Abstract (English)
Recently, numerous deep models have been proposed to enhance the performance of multivariate time series (MTS) forecasting. Among them, Graph Neural Networks (GNNs)-based methods have shown great potential due to their capability to explicitly model inter-variable dependencies. However, these methods often overlook the diversity of information among neighbors, which may lead to redundant information aggregation. In addition, their final prediction typically relies solely on the representation from a single temporal scale. To tackle these issues, we propose a Graph Neural Networks (GNNs) with Diversity-aware Neighbor Selection and Dynamic Multi-scale Fusion (DIMIGNN). DIMIGNN introduces a Diversity-aware Neighbor Selection Mechanism (DNSM) to ensure that each variable shares high informational similarity with its neighbors while maintaining diversity among neighbors themselves. Furthermore, a Dynamic Multi-Scale Fusion Module (DMFM) is introduced to dynamically adjust the contributions of prediction results from different temporal scales to the final forecasting result. Extensive experiments on real-world datasets demonstrate that DIMIGNN consistently outperforms prior methods.
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