基于位置感知图增强变分自编码器的网络化时间序列插补
Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
AI总结：
针对网络化时间序列（NTS）中图结构动态变化且存在缺失值的问题，提出PoGeVon模型，利用变分自编码器（VAE）结合基于带重启随机游走（RWR）的节点位置嵌入，通过多任务学习的3阶段解码器相互插补时间序列与图结构缺失值，实现了比基线更优的插补效果。
摘要：
多元时间序列（MTS）插补是近年来被广泛研究的问题。现有方法主要分为两大类：（1）主要关注时间序列特征的深度循环或生成模型，以及（2）基于图神经网络（GNNs）的模型，该模型利用MTS固有图结构的拓扑信息作为关系归纳偏置进行插补。然而，这些方法要么忽略了拓扑信息，要么假设图结构是固定且准确已知的。因此，在更具挑战性的MTS数据（如网络化时间序列，NTS）中，它们无法充分利用图动态进行精确插补，其中底层图不断变化且可能存在缺失边。在本文中，我们提出了一种新方法来克服这些局限性。首先，我们定义了NTS上的插补问题，该问题在节点时间序列特征和图结构中均包含缺失值。然后，我们设计了一个名为PoGeVon的新模型，该模型利用变分自编码器（VAE）来预测节点时间序列特征和图结构上的缺失值。具体而言，我们在编码器中提出了一种基于带重启的随机游走（RWR）的新节点位置嵌入，与基于消息传递的图神经网络（GNNs）相比，具有可证明的更高表达能力。我们进一步设计了一个从多任务学习角度进行3阶段预测的解码器，以相互插补时间序列和图结构中的缺失值。实验结果证明了我们的模型相对于基线的有效性。
英文摘要：
Multivariate time series (MTS) imputation is a widely studied problem in recent years. Existing methods can be divided into two main groups, including (1) deep recurrent or generative models that primarily focus on time series features, and (2) graph neural networks (GNNs) based models that utilize the topological information from the inherent graph structure of MTS as relational inductive bias for imputation. Nevertheless, these methods either neglect topological information or assume the graph structure is fixed and accurately known. Thus, they fail to fully utilize the graph dynamics for precise imputation in more challenging MTS data such as networked time series (NTS), where the underlying graph is constantly changing and might have missing edges. In this paper, we propose a novel approach to overcome these limitations. First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures. Then, we design a new model named PoGeVon which leverages variational autoencoder (VAE) to predict missing values over both node time series features and graph structures. In particular, we propose a new node position embedding based on random walk with restart (RWR) in the encoder with provable higher expressive power compared with message-passing based graph neural networks (GNNs). We further design a decoder with 3-stage predictions from the perspective of multi-task learning to impute missing values in both time series and graph structures reciprocally. Experiment results demonstrate the effectiveness of our model over baselines.
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。
