HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Transactions on Machine Learning Research Journal Année : 2023

HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series

Résumé

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.
Fichier principal
Vignette du fichier
hermes_paper.pdf (2.94 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03559178 , version 1 (06-02-2022)
hal-03559178 , version 2 (13-02-2022)
hal-03559178 , version 3 (08-09-2023)

Identifiants

Citer

Etienne David, Jean Bellot, Sylvain Le Corff. HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series. Transactions on Machine Learning Research Journal, 2023. ⟨hal-03559178v3⟩
65 Consultations
75 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More