  New fashion product sales forecasting is a challenging problem that involves
many business dynamics and cannot be solved by classical forecasting
approaches. In this paper, we investigate the effectiveness of systematically
probing exogenous knowledge in the form of Google Trends time series and
combining it with multi-modal information related to a brand-new fashion item,
in order to effectively forecast its sales despite the lack of past data. In
particular, we propose a neural network-based approach, where an encoder learns
a representation of the exogenous time series, while the decoder forecasts the
sales based on the Google Trends encoding and the available visual and metadata
information. Our model works in a non-autoregressive manner, avoiding the
compounding effect of large first-step errors. As a second contribution, we
present VISUELLE, a publicly available dataset for the task of new fashion
product sales forecasting, containing multimodal information for 5577 real, new
products sold between 2016-2019 from Nunalie, an Italian fast-fashion company.
The dataset is equipped with images of products, metadata, related sales, and
associated Google Trends. We use VISUELLE to compare our approach against
state-of-the-art alternatives and several baselines, showing that our neural
network-based approach is the most accurate in terms of both percentage and
absolute error. It is worth noting that the addition of exogenous knowledge
boosts the forecasting accuracy by 1.5% in terms of Weighted Absolute
Percentage Error (WAPE), revealing the importance of exploiting informative
external information. The code and dataset are both available at
https://github.com/HumaticsLAB/GTM-Transformer.
