  Deep learning applied to weather forecasting has started gaining popularity
because of the progress achieved by data-driven models. The present paper
compares two different deep learning architectures to perform weather
prediction on daily data gathered from 18 cities across Europe and spanned over
a period of 15 years. We propose the Deep Attention Unistream Multistream
(DAUM) networks that investigate different types of input representations (i.e.
tensorial unistream vs. multistream ) as well as the incorporation of the
attention mechanism. In particular, we show that adding a self-attention block
within the models increases the overall forecasting performance. Furthermore,
visualization techniques such as occlusion analysis and score maximization are
used to give an additional insight on the most important features and cities
for predicting a particular target feature of target cities.
