采用多样性感知邻居选择与动态多尺度融合的图神经网络用于多元时间序列预测
Graph Neural Networks with Diversity-aware Neighbor Selection and Dynamic Multi-scale Fusion for Multivariate Time Series Forecasting
AI总结：
针对GNN多元时间序列预测中邻居信息冗余和单一时间尺度表示的问题，提出DIMIGNN，通过DNSM选择兼顾相似性与多样性的邻居，并利用DMFM动态融合多尺度预测，实验证明其性能持续优于已有方法。
摘要：
近年来，人们提出了大量深度模型以提升多元时间序列（MTS）预测的性能。其中，基于图神经网络（GNNs）的方法因其能够显式建模变量间依赖关系而展现出巨大潜力。然而，这些方法往往忽视邻居之间信息的多样性，这可能导致冗余的信息聚合。此外，它们的最终预测通常仅依赖来自单一时间尺度的表示。为解决这些问题，我们提出了一种具有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引入了多样性感知邻居选择机制（DNSM），以确保每个变量与其邻居具有较高的信息相似性，同时保持邻居彼此之间的多样性。此外，本文还引入了动态多尺度融合模块（DMFM），用于动态调整来自不同时间尺度的预测结果对最终预测结果的贡献。在真实世界数据集上的大量实验表明，DIMIGNN始终优于以往方法。
英文摘要：
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.
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。
