{"title":"新时尚产品的需求预测:综述论文","authors":"Anitha S., Neelakandan R.","doi":"10.1002/for.3192","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Demand Forecasting New Fashion Products: A Review Paper\",\"authors\":\"Anitha S., Neelakandan R.\",\"doi\":\"10.1002/for.3192\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":47835,\"journal\":{\"name\":\"Journal of Forecasting\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1002/for.3192\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1002/for.3192","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
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.
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.