The fashion industry is one of the most active and competitive markets in the world, manufacturing millions of products and reaching large audiences every year. A plethora of business processes are involved in this large-scale industry, but due to the generally short life-cycle of clothing items, supply-chain management and retailing strategies are crucial for good market performance. Correctly understanding the wants and needs of clients, managing logistic issues and marketing the correct products are high-level problems with a lot of uncertainty associated to them given the number of influencing factors, but most importantly due to the unpredictability often associated with the future. It is therefore straightforward that forecasting methods, which generate predictions of the future, are indispensable in order to ameliorate all the various business processes that deal with the true purpose and meaning of fashion: having a lot of people wear a particular product or style, rendering these items, people and consequently brands fashionable. In this paper, we provide an overview of three concrete forecasting tasks that any fashion company can apply in order to improve their industrial and market impact. We underline advances and issues in all three tasks and argue about their importance and the impact they can have at an industrial level. Finally, we highlight issues and directions of future work, reflecting on how learning-based forecasting methods can further aid the fashion industry.

传统的自动门不能区分希望穿过门和经过门的人们，因此他们经常不必要地打开。这导致需要在商业和非商业环境中采用新系统：智能门。特别是，智能门系统根据周围环境的社会环境预测了门附近的人们的意图，然后就是否打开门做出合理的决定。这项工作提出了与智能门有关的第一张纸张，没有铃铛和哨子。我们首先指出，问题不仅涉及可靠性，气候控制，安全性和操作方式。的确，通过对近亲学和场景推理的复杂结合分析，一种预测门附近人们意图的系统还涉及对场景的社会背景的更深入了解。此外，我们对自动门进行了详尽的文献综述，提供了一种新型的系统配方。此外，我们对智能门的未来应用，道德缺陷的描述和立法问题进行了分析。

我们提出了一个以数据为中心的管道，能够为新的时尚产品性能预测（NFPPF）问题生成外源性观察数据，即预测没有过去观察的全新服装探测的性能。我们的管道从一件服装探针的单个可用图像开始制造了失踪的过去。它首先要扩展与图像关联的文本标签，在过去的特定时间上查询相关的时尚图像或不合时宜的图像。通过自信的学习，可以在这些网络图像上对二进制分类器进行良好的训练，以了解过去的时尚以及探测图像对这种时尚性的概念的符合。这种合规性产生了潜在的性能（POP）时间序列，表明如果探针的性能较早，则该探针的性能如何。 POP被证明是对探针未来表现的高度预测，可以改善最近Visuelle快速时尚数据集中所有最先进模型的销售预测。我们还表明，流行音乐反映了时尚前锋基准上的新样式（服装合奏）的基础真实性的普及，这表明我们的熟悉的信号是一个真实的流行，每个人都可以访问，并且可以在任何分析时间范围内获得普遍性。 。预测代码，数据和流行时间序列可在以下网址获得：https：//github.com/humaticslab/pop-mining-potential-performance

We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie, a famous Italian company with hundreds of shops located in different areas within the country. In particular, we focus on a specific prediction problem, namely short-observation new product sale forecasting (SO-fore). SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores. The goal is to forecast the sales for a particular horizon, given a short, available past (few weeks), since no earlier statistics are available. To be successful, SO-fore approaches should capture this short past and exploit other modalities or exogenous data. To these aims, Visuelle 2.0 is equipped with disaggregated data at the item-shop level and multi-modal information for each clothing item, allowing computer vision approaches to come into play. The main message that we deliver is that the use of image data with deep networks boosts performances obtained when using the time series in long-term forecasting scenarios, ameliorating the WAPE and MAE by up to 5.48% and 7% respectively compared to competitive baseline methods. The dataset is available at https://humaticslab.github.io/forecasting/visuelle

新的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Google趋势时间序列的形式进行系统探索外源知识的有效性，并将其与与全新时尚项目相关的多模式信息结合在一起，以便有效地预测其销售额，尽管缺乏过去数据。特别是，我们提出了一种基于神经网络的方法，编码器在其中学习了外源时间序列的表示，而解码器则根据Google趋势编码以及可用的视觉和元数据信息来预测销售。我们的模型以非自动回归方式起作用，避免了大型第一步错误的复合效果。作为第二个贡献，我们介绍了Visuelle，这是一个公开可用的数据集，用于新时尚产品销售预测的任务，其中包含5577 Real，新产品的多模式信息，该产品在2016 - 2019年之间从意大利快速时尚公司Nunalie出售。该数据集配备了产品，元数据，相关销售以及相关的Google趋势的图像。我们使用Visuelle将我们的方法与最新的替代方案和几种基线进行比较，这表明我们基于神经网络的方法在百分比和绝对错误方面都是最准确的。值得注意的是，外源知识的添加使预测准确性提高了1.5％的Wape，从而揭示了利用内容丰富的外部信息的重要性。代码和数据集均可在https://github.com/humaticslab/gtm-transformer上获得。