  The ability to predict, anticipate and reason about future outcomes is a key
component of intelligent decision-making systems. In light of the success of
deep learning in computer vision, deep-learning-based video prediction emerged
as a promising research direction. Defined as a self-supervised learning task,
video prediction represents a suitable framework for representation learning,
as it demonstrated potential capabilities for extracting meaningful
representations of the underlying patterns in natural videos. Motivated by the
increasing interest in this task, we provide a review on the deep learning
methods for prediction in video sequences. We firstly define the video
prediction fundamentals, as well as mandatory background concepts and the most
used datasets. Next, we carefully analyze existing video prediction models
organized according to a proposed taxonomy, highlighting their contributions
and their significance in the field. The summary of the datasets and methods is
accompanied with experimental results that facilitate the assessment of the
state of the art on a quantitative basis. The paper is summarized by drawing
some general conclusions, identifying open research challenges and by pointing
out future research directions.
