Environmental monitoring is crucial to our understanding of climate change, biodiversity loss and pollution. The availability of large-scale spatio-temporal data from sources such as sensors and satellites allows us to develop sophisticated models for forecasting and understanding key drivers. However, the data collected from sensors often contain missing values due to faulty equipment or maintenance issues. The missing values rarely occur simultaneously leading to data that are multivariate misaligned sparse time series. We propose two models that are capable of performing multivariate spatio-temporal forecasting while handling missing data naturally without the need for imputation. The first model is a transformer-based model, which we name SERT (Spatio-temporal Encoder Representations from Transformers). The second is a simpler model named SST-ANN (Sparse Spatio-Temporal Artificial Neural Network) which is capable of providing interpretable results. We conduct extensive experiments on two different datasets for multivariate spatio-temporal forecasting and show that our models have competitive or superior performance to those at the state-of-the-art.

翻译：环境监测对于我们理解气候变化、生物多样性丧失和污染问题至关重要。来自传感器和卫星等来源的大规模时空数据的可用性，使我们能够开发用于预测和理解关键驱动因素的复杂模型。然而，由于设备故障或维护问题，从传感器收集的数据经常包含缺失值。这些缺失值很少同时出现，导致数据成为多变量未对齐的稀疏时间序列。我们提出了两种模型，能够自然地处理缺失数据，无需插值即可执行多变量时空预测。第一种模型是基于Transformer的模型，我们将其命名为SERT（来自Transformer的时空编码器表示）。第二种是更简单的模型，名为SST-ANN（稀疏时空人工神经网络），能够提供可解释的结果。我们在两个不同的数据集上进行了广泛的多变量时空预测实验，结果表明我们的模型具有与现有最优方法相当或更优的性能。