Both procedures tend to be excellent for prediction purposes over small time horizons, but are generally time-consuming and, crucially, do not provide a global prior model for the temporally-varying dynamics that is realistic.
This task is challenging due to the complicated spatiotemporal dependencies among regions.
The paper presents a spatio-temporal wind speed forecasting algorithm using Deep Learning (DL)and in particular, Recurrent Neural Networks(RNNs).
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
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