利用多变量长期历史表征进行时间序列预测
Leveraging Multivariate Long-Term History Representation for Time Series Forecasting
AI总结：
针对现有多变量时间序列预测STGNN忽略长期跨序列时空相关性的问题，提出含LHEncoder、HRetriever和TAggregator的LMHR增强框架，在多个真实数据集上显著提升预测精度。
摘要：
多变量时间序列（MTS）预测在工业界和学术界都有广泛应用。时空图神经网络（STGNN）的最新进展在建模时空相关性方面取得了巨大进展。受计算复杂度限制，大多数用于MTS预测的STGNN主要关注短期和局部的时空依赖关系。尽管近期一些方法尝试将单变量历史纳入建模，但它们仍忽略了MTS中关键的长期时空相似性与相关性，而这些因素对准确预测至关重要。为填补这一空白，我们提出了一种名为长期多变量历史表征（LMHR）增强型STGNN的MTS预测框架。具体而言，我们采用长期历史编码器（LHEncoder），将长期历史有效编码为段级上下文表征并降低点级噪声。我们设计了一种非参数化的分层表征检索器（HRetriever），将空间信息纳入长期时空依赖建模中，且无需额外训练即可筛选出最有价值的表征。基于Transformer的聚合器（TAggregator）通过排序位置嵌入，高效地选择性融合稀疏检索到的上下文表征。实验结果表明，在多个真实世界数据集上，LMHR在平均预测时长上优于典型STGNN 10.72%，优于现有最优方法4.12%。此外，在所有数据集中排名前10%的快速变化模式上，它始终将预测准确率提升了9.8%。
英文摘要：
Multivariate Time Series (MTS) forecasting has a wide range of applications in both industry and academia. Recent advances in Spatial-Temporal Graph Neural Network (STGNN) have achieved great progress in modelling spatial-temporal correlations. Limited by computational complexity, most STGNNs for MTS forecasting focus primarily on short-term and local spatial-temporal dependencies. Although some recent methods attempt to incorporate univariate history into modeling, they still overlook crucial long-term spatial-temporal similarities and correlations across MTS, which are essential for accurate forecasting. To fill this gap, we propose a framework called the Long-term Multivariate History Representation (LMHR) Enhanced STGNN for MTS forecasting. Specifically, a Long-term History Encoder (LHEncoder) is adopted to effectively encode the long-term history into segment-level contextual representations and reduce point-level noise. A non-parametric Hierarchical Representation Retriever (HRetriever) is designed to include the spatial information in the long-term spatial-temporal dependency modelling and pick out the most valuable representations with no additional training. A Transformer-based Aggregator (TAggregator) selectively fuses the sparsely retrieved contextual representations based on the ranking positional embedding efficiently. Experimental results demonstrate that LMHR outperforms typical STGNNs by 10.72% on the average prediction horizons and state-of-the-art methods by 4.12% on several real-world datasets. Additionally, it consistently improves prediction accuracy by 9.8% on the top 10% of rapidly changing patterns across the datasets.
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。
