  Symbolic regression corresponds to an ensemble of techniques that allow to
uncover an analytical equation from data. Through a closed form formula, these
techniques provide great advantages such as potential scientific discovery of
new laws, as well as explainability, feature engineering as well as fast
inference. Similarly, deep learning based techniques has shown an extraordinary
ability of modeling complex patterns. The present paper aims at applying a
recent end-to-end symbolic regression technique, i.e. the equation learner
(EQL), to get an analytical equation for wind speed forecasting. We show that
it is possible to derive an analytical equation that can achieve reasonable
accuracy for short term horizons predictions only using few number of features.
