2022/02/07 by Étienne David, Jean Pierre Bellot, David, Etienne +5 · 2 citations
Business, Management and Accounting · Computer Science · Engineering · #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Generative Adversarial Networks and Image Synthesis #Machine Learning (stat.ML) #Signal Processing (eess.SP) #Statistics Theory (math.ST) #Time Series Analysis and Forecasting #Traffic Prediction and Management Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2202.03224
openalex publication_date 2022/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.