层次化联合图学习与多元时间序列预测
Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting
AI总结：
本文提出将多元时间序列建模为图节点，结合图神经网络、注意力机制和层次化信号分解捕获多种时空依赖，在多个长期预测基准上相比现有模型平均降低23%的MSE。
摘要：
多元时间序列广泛存在于许多科学和工业领域。由于多元信号具有长期时间依赖性和复杂的直接与间接交互，对其进行建模具有挑战性。为应对这些复杂性，我们提出了一种将多元信号表示为图中节点的方法，其中边表示它们之间的相互依赖关系。具体而言，我们利用图神经网络（GNN）和注意力机制高效地学习时间序列数据中的潜在关系。此外，我们建议采用在图结构上运行的层次化信号分解来捕获多种空间依赖关系。我们提出的模型在多个用于长期预测任务的真实世界基准数据集上进行了有效性评估。结果一致展示了我们模型的优越性，与现有模型相比，均方误差（MSE）平均降低了23%。
英文摘要：
Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intricate interactions--both direct and indirect. To confront these complexities, we introduce a method of representing multivariate signals as nodes in a graph with edges indicating interdependency between them. Specifically, we leverage graph neural networks (GNN) and attention mechanisms to efficiently learn the underlying relationships within the time series data. Moreover, we suggest employing hierarchical signal decompositions running over the graphs to capture multiple spatial dependencies. The effectiveness of our proposed model is evaluated across various real-world benchmark datasets designed for long-term forecasting tasks. The results consistently showcase the superiority of our model, achieving an average 23\% reduction in mean squared error (MSE) compared to existing models.
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。
