  In this paper, we address the issue of short-term wind speed prediction at a
given site. We show that, when one uses spatiotemporal information as provided
by wind data of neighboring stations, one significantly improves the prediction
quality. Our methodology does not focus on any peculiar forecasting model but
rather considers a set of various prediction methods, from a very basic linear
regression to different machine learning models. In each case, our approach
consists in specifically and incrementally studying the benefits of using wind
data of the surrounding stations. We show that, at all horizons ranging from 1
to 6 hours ahead, the relative gain on the RMSE of the predicted wind speed can
increase up to 20 %. For all the considered forecasting methods, we show that
such a gain is far better than the one obtained by considering other kind of
information like local weather variables or seeking for an optimal deep
learning model. Moreover we provide evidence that non-linear models, as neural
networks or gradient boosting methods, significantly outperform linear
regression. These conclusions are simply interpreted as resulting from the
ability of a method to capture the transport of the information by the main
flow in the upwind direction.
