We introduce the asynchronous graph generator (AGG), a novel graph neural
network architecture for multi-channel time series which models observations as
nodes on a dynamic graph and can thus perform data imputation by transductive
node generation. Completely free from recurrent components or assumptions about
temporal regularity, AGG represents measurements, timestamps and metadata
directly in the nodes via learnable embeddings, to then leverage attention to
learn expressive relationships across the variables of interest. This way, the
proposed architecture implicitly learns a causal graph representation of sensor
measurements which can be conditioned on unseen timestamps and metadata to
predict new measurements by an expansion of the learnt graph. The proposed AGG
is compared both conceptually and empirically to previous work, and the impact
of data augmentation on the performance of AGG is also briefly discussed. Our
experiments reveal that AGG achieved state-of-the-art results in time series
data imputation, classification and prediction for the benchmark datasets
Beijing Air Quality, PhysioNet Challenge 2012 and UCI localisation.

我们介绍了一种新颖的异步图生成器（AGG），它是一种多通道时间序列的图神经网络体系结构，通过将观测值建模为动态图上的节点，并通过传导节点生成来执行数据插补。AGG 在节点中直接使用可学习的嵌入表示测量值、时间戳和元数据，然后利用注意力来学习感兴趣的变量之间的表达关系，从而隐含地学习传感器测量值的因果图表示。该模型在北京空气质量、PhysioNet 2012 挑战和 UCI 本地化的基准数据集上，在时间序列数据插补、分类和预测方面取得了最先进的结果。