Given a set of synchronous time series, each associated with a sensor-point in space and characterized by inter-series relationships, the problem of spatiotemporal forecasting consists of predicting future observations for each point. Spatiotemporal graph neural networks achieve striking results by representing the relationships across time series as a graph. Nonetheless, most existing methods rely on the often unrealistic assumption that inputs are always available and fail to capture hidden spatiotemporal dynamics when part of the data is missing. In this work, we tackle this problem through hierarchical spatiotemporal downsampling. The input time series are progressively coarsened over time and space, obtaining a pool of representations that capture heterogeneous temporal and spatial dynamics. Conditioned on observations and missing data patterns, such representations are combined by an interpretable attention mechanism to generate the forecasts. Our approach outperforms state-of-the-art methods on synthetic and real-world benchmarks under different missing data distributions, particularly in the presence of contiguous blocks of missing values.

翻译：给定一组同步时间序列，每个序列与空间中的一个传感器点相关联，并具有序列间关系特征，时空预测问题旨在预测每个点的未来观测值。时空图神经网络通过将时间序列间的关系表示为图，取得了显著的效果。然而，现有大多数方法依赖于输入始终可用的这一通常不切实际的假设，当部分数据缺失时，无法捕捉隐藏的时空动态。在本研究中，我们通过分层时空下采样解决该问题。输入时间序列在时间和空间上逐步粗化，获得一组捕获异质时间和空间动态的表征。这些表征在观测值和缺失数据模式的条件下，通过可解释的注意力机制进行组合以生成预测。我们的方法在合成和真实世界基准测试中，在不同的缺失数据分布下，特别是在存在连续缺失值块的情况下，优于现有最先进方法。