A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales

1 code implementation • 26 Aug 2021 • Yihao Hu, Fearghal O'Donncha, Paulito Palmes, Meredith Burke, Ramon Filgueira, Jon Grant

Enabling learning across the spatial and temporal directions, this paper addresses two fundamental challenges of ML applications to environmental science: 1) data sparsity and the challenges and costs of collecting measurements of environmental conditions such as ocean dynamics, and 2) environmental datasets are inherently connected in the spatial and temporal directions while classical ML approaches only consider one of these directions.

Time Series Forecasting