HERMES：包含外部信号的非平稳时尚时间序列混合纠错模型
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series
AI总结：
针对非平稳时尚时间序列预测问题，提出结合参数模型与全局循环神经网络的混合预测模型HERMES，并公开包含10000条时间序列及影响者外部弱信号的数据集，在多个基准上取得最优结果。
摘要：
开发预测非平稳时间序列的模型和算法是一个长期存在的统计问题。这对于许多应用至关重要，尤其是时尚或零售行业，以便做出最优库存决策并避免大量浪费。通过使用最先进的计算机视觉方法追踪社交媒体上的数千种时尚趋势，我们提出了一种用于时尚时间序列预测的新模型。我们的贡献有两方面。首先，我们公开发布了一个包含10000条每周时尚时间序列的数据集。由于影响动态是新兴趋势检测的关键，我们为每条时间序列关联了一个代表影响者行为的外部弱信号。其次，为了利用此类数据集，我们提出了一种新的混合预测模型。该方法将包含季节性成分的每条时间序列参数模型与全局循环神经网络相结合，以纳入零星的外部信号。该混合模型在所提出的时尚数据集以及M4竞赛的每周时间序列上提供了最先进的结果，并证明了引入外部弱信号的益处。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
