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：环境监测对于理解气候变化、生物多样性丧失和污染问题至关重要。传感器和卫星等来源提供的海量时空数据，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，传感器采集的数据常因设备故障或维护问题而存在缺失值。这些缺失值极少同时出现，导致数据形成多变量错位稀疏时间序列。我们提出了两种无需插值即可自然处理缺失数据、并能执行多变量时空预测的模型。第一种是基于Transformer的模型，名为SERT（源自Transformer的时空编码表示）；第二种是更简洁的模型SST-ANN（稀疏时空人工神经网络），能够提供可解释的结果。我们在两个不同数据集上进行了多变量时空预测的广泛实验，结果表明，我们的模型性能达到或超越了当前最先进水平。