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 5,577 real, new products sold between 2016 and 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% in terms of Weighted Absolute Percentage Error (WAPE), revealing the importance of exploiting informative external information. The code and dataset are both available online (at https://github.com/HumaticsLAB/GTM-Transformer).

中文翻译：

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谷歌搜索成功就成功了一半：利用基于图像的谷歌趋势对新时尚产品销售进行多模式预测

新时尚产品销售预测是一个具有挑战性的问题，涉及许多业务动态，无法通过经典的预测方法来解决。在本文中，我们研究了以谷歌趋势时间序列的形式系统地探索外生知识并将其与与全新时尚单品相关的多模态信息相结合的有效性，以便在缺乏过去的时尚单品的情况下有效地预测其销售情况。数据。特别是，我们提出了一种基于神经网络的方法，其中编码器学习外源时间序列的表示，而解码器根据谷歌趋势编码以及可用的视觉和元数据信息预测销售。我们的模型以非自回归方式工作，避免了较大的第一步误差的复合效应。作为第二个贡献，我们提出了 VISUELLE，这是一个用于新时尚产品销售预测任务的公开数据集，包含意大利快时尚公司 Nunalie 在 2016 年至 2019 年间销售的 5,577 种真实新产品的多模态信息。该数据集配备了产品图像、元数据、相关销售和相关的 Google 趋势。我们使用 VISUELLE 将我们的方法与最先进的替代方案和几个基线进行比较，表明我们基于神经网络的方法在百分比和绝对误差方面都是最准确的。值得注意的是，外生知识的加入使加权绝对百分比误差（WAPE）的预测精度提高了 1.5%，揭示了利用丰富的外部信息的重要性。代码和数据集均可在线获取（位于https://github.com/HumaticsLAB/GTM-Transformer）。