HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series
Abstract: 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 mode. 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.
Submission Length: Long submission (more than 12 pages of main content)
Changes Since Last Submission: Accepted version of the HERMES paper
Code: https://github.com/etidav/HERMES
Assigned Action Editor: ~Makoto_Yamada3
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 855
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