  Developing models and algorithms to predict nonstationary time series is a
long standing statistical problem. It is crucial for many applications, in
particular for fashion or retail industries, to make optimal inventory
decisions and avoid massive wastes. By tracking thousands of fashion trends on
social media with state-of-the-art computer vision approaches, we propose a new
model for fashion time series forecasting. Our contribution is twofold. We
first provide publicly a dataset gathering 10000 weekly fashion time series. As
influence dynamics are the key of emerging trend detection, we associate with
each time series an external weak signal representing behaviours of
influencers. Secondly, to leverage such a dataset, we propose a new hybrid
forecasting model. Our approach combines per-time-series parametric models with
seasonal components and a global recurrent neural network to include sporadic
external signals. This hybrid model provides state-of-the-art results on the
proposed fashion dataset, on the weekly time series of the M4 competition, and
illustrates the benefit of the contribution of external weak signals.
