A networked time series (NETS) is a family of time series on a given graph, one for each node. It has found a wide range of applications from intelligent transportation, environment monitoring to mobile network management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, etc. In this paper, we study the problem of NETS prediction with incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose novel Graph Temporal Attention Networks by incorporating the attention mechanism to capture both inter-time series correlations and temporal correlations. We conduct extensive experiments on three real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods except when data exhibit very low variance, in which case NETS-ImpGAN still achieves competitive performance.

翻译：网络化时间序列是定义在给定图上一系列时间序列的集合，每个节点对应一个时间序列。它在智能交通、环境监测到移动网络管理等诸多领域具有广泛应用。此类应用中的一项核心任务是基于历史数值与底层图结构预测网络化时间序列的未来值。现有方法大多需要完整数据进行训练。然而在真实场景中，由于传感器故障、感知覆盖不全等原因，数据缺失问题十分常见。本文研究不完整数据下的网络化时间序列预测问题。我们提出NETS-ImpGAN，一种新颖的深度学习框架，可在历史与未来均存在缺失值的不完整数据上进行训练。进一步，我们通过融合注意力机制提出图时序注意力网络，以捕获时间序列间相关性与时间相关性。我们在三个真实数据集上针对不同缺失模式与缺失率进行了广泛实验。实验结果表明，NETS-ImpGAN在多数场景下优于现有方法，仅当数据方差极低时其仍能取得具有竞争力的性能。