  We present Visuelle 2.0, the first dataset useful for facing diverse
prediction problems that a fast-fashion company has to manage routinely.
Furthermore, we demonstrate how the use of computer vision is substantial in
this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing
products of Nuna Lie, a famous Italian company with hundreds of shops located
in different areas within the country. In particular, we focus on a specific
prediction problem, namely short-observation new product sale forecasting
(SO-fore). SO-fore assumes that the season has started and a set of new
products is on the shelves of the different stores. The goal is to forecast the
sales for a particular horizon, given a short, available past (few weeks),
since no earlier statistics are available. To be successful, SO-fore approaches
should capture this short past and exploit other modalities or exogenous data.
To these aims, Visuelle 2.0 is equipped with disaggregated data at the
item-shop level and multi-modal information for each clothing item, allowing
computer vision approaches to come into play. The main message that we deliver
is that the use of image data with deep networks boosts performances obtained
when using the time series in long-term forecasting scenarios, ameliorating the
WAPE and MAE by up to 5.48% and 7% respectively compared to competitive
baseline methods. The dataset is available at
https://humaticslab.github.io/forecasting/visuelle
