  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.
