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Forecasting New Apparel Sales Using Deep Learning and Nonlinear Neural Network Regression
University of Borås, Faculty of Textiles, Engineering and Business.
University of Borås, Faculty of Textiles, Engineering and Business.
2019 (English)In: 2019 International Conference on Engineering, Science, and Industrial Applications (ICESI), 2019Conference paper, Published paper (Refereed)
Abstract [en]

Compared to other retail industries, fashion retail industry faces many challenges to foresee future demand of its products. This is due to ever-changing choices of their consumers, who get influenced by rapidly changing market trends and it leads to the short life cycle of a fashion product. Due to the advent of e-commerce business models, fashion retailers have to put a multitude of virtual product images along with their feature information on their websites in order for their customers to know the fashion products and improve their purchasing experience. It is imperative for fashion retailers to predict future consumer preferences in advance; however, they lack advanced tools to achieve this goal. To overcome this problem, this research work combines the historical information of products with their image features using deep learning and predicts future sales. Apparel images are converted into feature vectors and then are merged with historical sales data. We applied backward propagation neural network model to predict the sales of a new product. It is found that the model performs quite well despite the small size of the dataset. This approach could be promising for forecasting the new arrivals of apparels in the market, and fashion retailers could improve their efficiency and growth.

Place, publisher, year, edition, pages
2019.
Series
2019 International Conference on Engineering, Science, and Industrial Applications (ICESI), ISSN 2521-3814
Keywords [en]
Forecasting, Deep Learning, Neural Network, Apparel Industry
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hb:diva-22822DOI: 10.1109/ICESI.2019.8863024Scopus ID: 2-s2.0-85073872059ISBN: 978-1-7281-2174-1 (print)ISBN: 978-1-7281-2173-4 (electronic)OAI: oai:DiVA.org:hb-22822DiVA, id: diva2:1393435
Conference
2019 International Conference on Engineering, Science, and Industrial Applications (ICESI), Tokyo, August 22-24, 2019.
Funder
EU, European Research CouncilAvailable from: 2020-02-16 Created: 2020-02-16 Last updated: 2024-02-01

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fulltext(710 kB)441 downloads
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Publisher's full textScopushttps://ieeexplore.ieee.org/document/8863024

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Giri, ChandadeviBalkow, Jenny

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