DeepHGNN：基于图神经网络的层级相关多元时间序列预测方法研究
DeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series
AI总结：
本文提出DeepHGNN框架，通过基于图的层级插值和端到端协调机制，利用各层级间可预测性差异共享信号，解决了层级时间序列预测中准确性与一致性的挑战，显著提升了预测性能。
摘要：
图神经网络（GNN）在预测领域获得了显著关注，特别是其同时考虑序列内时间相关性和序列间关系的能力。本文提出了一种新颖的层级GNN（DeepHGNN）框架，专为复杂层级结构中的预测而设计。DeepHGNN的独特之处在于其创新的基于图的层级插值和端到端协调机制。该方法在各个层级之间共享信号的同时，确保了预测的准确性和一致性，解决了层级预测中的一个关键挑战。层级时间序列的一个关键洞察是各层级间可预测性的差异，上层通常包含更易预测的成分。DeepHGNN利用这一洞察，汇聚并利用所有层级知识，从而提升整体预测准确率。我们针对多个最先进模型的全面评估证实了DeepHGNN的优越性能。本研究不仅展示了DeepHGNN在显著提升预测准确率方面的有效性，也促进了对基于图方法的层级时间序列预测的理解。
英文摘要：
Graph Neural Networks (GNN) have gained significant traction in the forecasting domain, especially for their capacity to simultaneously account for intra-series temporal correlations and inter-series relationships. This paper introduces a novel Hierarchical GNN (DeepHGNN) framework, explicitly designed for forecasting in complex hierarchical structures. The uniqueness of DeepHGNN lies in its innovative graph-based hierarchical interpolation and an end-to-end reconciliation mechanism. This approach ensures forecast accuracy and coherence across various hierarchical levels while sharing signals across them, addressing a key challenge in hierarchical forecasting. A critical insight in hierarchical time series is the variance in forecastability across levels, with upper levels typically presenting more predictable components. DeepHGNN capitalizes on this insight by pooling and leveraging knowledge from all hierarchy levels, thereby enhancing the overall forecast accuracy. Our comprehensive evaluation set against several state-of-the-art models confirm the superior performance of DeepHGNN. This research not only demonstrates DeepHGNN's effectiveness in achieving significantly improved forecast accuracy but also contributes to the understanding of graph-based methods in hierarchical time series forecasting.
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。
