新时尚产品的需求预测:综述论文

IF 3.4 3区 经济学 Q1 ECONOMICS
Anitha S., Neelakandan R.
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引用次数: 0

摘要

新产品需求预测是一个重要但极具挑战性的过程,涉及多个领域。本文回顾了不同领域的各种预测模型,强调了预测新时尚产品所面临的独特挑战。这些挑战是多方面的,而且不断变化,包括消费者偏好、季节性和社交媒体的影响。了解了这些困难,我们就能为改进和灵活预测技术提供方法。机器学习技术有可能解决这些问题,并提高时尚产品需求预测的准确性。各种先进的算法,包括深度学习方法和集合方法,利用大型数据集和实时数据来准确预测需求模式。本文探讨了新产品预测中的独特挑战并研究了创新解决方案,为时尚行业的专家、研究人员和决策者提供了有价值的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Demand Forecasting New Fashion Products: A Review Paper
New product demand forecasting is an important but challenging process that extends to multiple sectors. The paper reviews various forecasting models across different domains, emphasizing the unique challenges of forecasting new fashion products. The challenges are multifaceted and subject to constant change, including consumer preferences, seasonality, and the influence of social media. Understanding such difficulties enables us to provide an approach for improved and flexible prediction techniques. Machine learning techniques have the potential to address these issues and improve the accuracy of fashion product demand forecasting. Various advanced algorithms, including deep learning approaches and ensemble methods, employ large datasets and real‐time data to predict demand patterns accurately. The paper suggests valuable information to experts, researchers, and decision‐makers in the fashion industry, as it addresses the unique challenges and examines innovative solutions in new product forecasting.
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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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