This paper proposes a new prediction process to explain and predict popularity evolution of YouTube videos. We exploit our recent study on the classification of YouTube videos in order to predict the evolution of videos' view-count. This classification allows to identify important factors of the observed popularity dynamics. Our experimental results show that our prediction process is able to reduce the average prediction errors compared to a state-of-the-art baseline model. We also evaluate the impact of adding popularity criteria in our classification.