  Long Short-Term Memory (LSTM) is a well-known method used widely on sequence
learning and time series prediction. In this paper we deployed stacked LSTM
model in an application of weather forecasting. We propose a 2-layer
spatio-temporal stacked LSTM model which consists of independent LSTM models
per location in the first LSTM layer. Subsequently, the input of the second
LSTM layer is formed based on the combination of the hidden states of the first
layer LSTM models. The experiments show that by utilizing the spatial
information the prediction performance of the stacked LSTM model improves in
most of the cases.
