HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series
HERMES:融合外部信号的非平稳时尚时间序列混合误差校正模型
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…