  Multivariate geo-sensory time series prediction is challenging because of the
complex spatial and temporal correlation. In urban water distribution systems
(WDS), numerous spatial-correlated sensors have been deployed to continuously
collect hydraulic data. Forecasts of monitored flow and pressure time series
are of vital importance for operational decision making, alerts and anomaly
detection. To address this issue, we proposed a hybrid dual-stage
spatial-temporal attention-based recurrent neural networks (hDS-RNN). Our model
consists of two stages: a spatial attention-based encoder and a temporal
attention-based decoder. Specifically, a hybrid spatial attention mechanism
that employs inputs along temporal and spatial axes is proposed. Experiments on
a real-world dataset are conducted and demonstrate that our model outperformed
9 baseline models in flow and pressure series prediction in WDS.
