论文标题
爱马仕：混合误差 - 矫正器模型，其中包含非组织时尚时间序列的外部信号
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series
论文作者
论文摘要
开发模型和算法来预测非组织时间序列是一个长期存在的统计问题。对于许多应用，尤其是时尚或零售行业，做出最佳库存决策并避免大量废物至关重要。通过使用最先进的计算机视觉方法在社交媒体上跟踪数千种时尚趋势，我们提出了一种新的时尚时间序列模型。我们的贡献是双重的。我们首先公开提供一个数据集收集，每周10000个时尚时间序列。由于影响动力学是新兴趋势检测的关键，因此我们将每个时间序列与代表影响者行为的外部弱信号相关联。其次，为了利用这样的数据集，我们提出了一个新的混合预测模型。我们的方法将每次系列参数模型与季节性组件和全球复发性神经网络结合在一起，其中包括零星的外部信号。该混合模型在M4竞赛的每周时间序列上为拟议的时尚数据集提供了最先进的结果，并说明了外部弱信号的贡献的好处。
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.
