New fashion product sales forecasting is a challenging problem that involves many business dynamics and cannot be solved by classical forecasting approaches. In this paper, we investigate the effectiveness of systematically probing exogenous knowledge in the form of Google Trends time series and combining it with multi-modal information related to a brand-new fashion item, in order to effectively forecast its sales despite the lack of past data. In particular, we propose a neural network-based approach, where an encoder learns a representation of the exogenous time series, while the decoder forecasts the sales based on the Google Trends encoding and the available visual and metadata information. Our model works in a non-autoregressive manner, avoiding the compounding effect of large first-step errors. As a second contribution, we present VISUELLE, a publicly available dataset for the task of new fashion product sales forecasting, containing multimodal information for 5577 real, new products sold between 2016-2019 from Nunalie, an Italian fast-fashion company. The dataset is equipped with images of products, metadata, related sales, and associated Google Trends. We use VISUELLE to compare our approach against state-of-the-art alternatives and several baselines, showing that our neural network-based approach is the most accurate in terms of both percentage and absolute error. It is worth noting that the addition of exogenous knowledge boosts the forecasting accuracy by 1.5% WAPE wise, revealing the importance of exploiting informative external information. The code and dataset are both available at https://github.com/HumaticsLAB/GTM-Transformer.
翻译:新时装产品销售预测是一个具有挑战性的问题,涉及许多商业动态,无法通过古典预测方法加以解决。在本文中,我们调查以谷歌趋势时间序列的形式系统探索外源知识的有效性,并将这种知识与与与与品牌新时装项目有关的多模式信息相结合,以有效预测其销售情况,尽管缺乏过去的数据。特别是,我们建议采用神经网络方法,让编码器了解外源时间序列的表示,而分解器则根据谷歌趋势编码和现有视觉和元数据信息预测销售情况。我们模型以非航空方式工作,避免了大规模第一步错误的复合效应。作为第二稿件,我们提供VISUELLE,这是为新时装产品销售预测任务公开提供的数据集,其中包含了5577种真实的多式联运信息,在2016-2019年期间从意大利快速时装公司Nunalie销售的新产品。该数据集配备了以产品、元数据编码、相关销售和相关的谷歌趋势为基础的产品图像。我们使用VISULLLLE来比较我们最准确的准确性网络和准确性数据定义,显示我们最准确性的数据基准值的外部数据。我们以显示的准确性网络和精确性数据定义的精确性选择。