  The fashion industry is one of the most active and competitive markets in the
world, manufacturing millions of products and reaching large audiences every
year. A plethora of business processes are involved in this large-scale
industry, but due to the generally short life-cycle of clothing items,
supply-chain management and retailing strategies are crucial for good market
performance. Correctly understanding the wants and needs of clients, managing
logistic issues and marketing the correct products are high-level problems with
a lot of uncertainty associated to them given the number of influencing
factors, but most importantly due to the unpredictability often associated with
the future. It is therefore straightforward that forecasting methods, which
generate predictions of the future, are indispensable in order to ameliorate
all the various business processes that deal with the true purpose and meaning
of fashion: having a lot of people wear a particular product or style,
rendering these items, people and consequently brands fashionable. In this
paper, we provide an overview of three concrete forecasting tasks that any
fashion company can apply in order to improve their industrial and market
impact. We underline advances and issues in all three tasks and argue about
their importance and the impact they can have at an industrial level. Finally,
we highlight issues and directions of future work, reflecting on how
learning-based forecasting methods can further aid the fashion industry.
