Deep shared representation learning for weather elements forecasting KU Leuven
© 2019 Elsevier B.V. The accuracy and reliability of weather forecasting are of importance for many economic, business and management activities. This paper introduces novel data-driven predictive models based on deep convolutional neural networks (CNN) architecture for temperature and wind speed prediction in weather data. In particular, the proposed deep learning framework employs different upgrading versions of the convolutional neural ...