Well googled is half done: Multimodal forecasting of new fashion product sales with image-based google trends

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-03-07 DOI:10.1002/for.3104
Geri Skenderi, Christian Joppi, Matteo Denitto, Marco Cristani
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Abstract

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).

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google好,就是成功了一半:利用基于图像的谷歌趋势对时尚新品销售进行多模态预测
新款时尚产品的销售预测是一个具有挑战性的问题,它涉及许多商业动态,传统预测方法无法解决。在本文中,我们研究了以谷歌趋势时间序列的形式系统探测外生知识的有效性,并将其与与全新时尚产品相关的多模态信息相结合,从而在缺乏过去数据的情况下有效预测其销售情况。具体而言,我们提出了一种基于神经网络的方法,其中编码器学习外生时间序列的表示,而解码器则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情况。我们的模型以非自回归的方式运行,避免了较大的第一步误差带来的复合效应。作为第二项贡献,我们介绍了 VISUELLE,这是一个用于新时尚产品销售预测任务的公开可用数据集,包含意大利快速时尚公司 Nunalie 在 2016 年至 2019 年期间售出的 5,577 件真实新产品的多模态信息。数据集包含产品图片、元数据、相关销售和相关谷歌趋势。我们使用 VISUELLE 将我们的方法与最先进的替代方法和几种基线进行了比较,结果表明我们基于神经网络的方法在百分比误差和绝对误差方面都是最准确的。值得注意的是,在加权绝对误差(WAPE)方面,增加外源知识可将预测准确率提高 1.5%,这揭示了利用信息丰富的外部信息的重要性。代码和数据集均可在线获取(网址:https://github.com/HumaticsLAB/GTM-Transformer)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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