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，其中每个序列与空间中的传感器点相关联并具有序列间关联特性，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结构，取得了显著成果。然而，现有方法大多依赖于输入数据始终可用的理想化假设，当部分数据缺失时无法捕捉隐藏的时空动态特征。本研究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维度上逐步粗化，获得能捕捉异质时空动态的表示池。这些表示在观测数据和缺失数据模式的条件下，通过可解释的注意力机制进行融合以生成预测。在不同缺失数据分布（特别是存在连续缺失值块）的合成与真实基准测试中，本方法均优于现有最优方法。