{"title":"用相关数据预测分层时间序列的个体和组合方法：一项实证研究","authors":"H. Rehman, Guohua Wan, A. Ullah, Badiea Shaukat","doi":"10.1080/23270012.2019.1629342","DOIUrl":null,"url":null,"abstract":"Hierarchical time series arise in manufacturing and service industries when the products or services have the hierarchical structure, and top-down and bottom-up methods are commonly used to forecas...","PeriodicalId":46290,"journal":{"name":"Journal of Management Analytics","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2019-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23270012.2019.1629342","citationCount":"4","resultStr":"{\"title\":\"Individual and combination approaches to forecasting hierarchical time series with correlated data: an empirical study\",\"authors\":\"H. Rehman, Guohua Wan, A. Ullah, Badiea Shaukat\",\"doi\":\"10.1080/23270012.2019.1629342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hierarchical time series arise in manufacturing and service industries when the products or services have the hierarchical structure, and top-down and bottom-up methods are commonly used to forecas...\",\"PeriodicalId\":46290,\"journal\":{\"name\":\"Journal of Management Analytics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2019-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/23270012.2019.1629342\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Management Analytics\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1080/23270012.2019.1629342\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Management Analytics","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1080/23270012.2019.1629342","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
Individual and combination approaches to forecasting hierarchical time series with correlated data: an empirical study
Hierarchical time series arise in manufacturing and service industries when the products or services have the hierarchical structure, and top-down and bottom-up methods are commonly used to forecas...
期刊介绍：
The Journal of Management Analytics (JMA) is dedicated to advancing the theory and application of data analytics in traditional business fields. It focuses on the intersection of data analytics with key disciplines such as accounting, finance, management, marketing, production/operations management, and supply chain management. JMA is particularly interested in research that explores the interface between data analytics and these business areas. The journal welcomes studies employing a range of research methods, including empirical research, big data analytics, data science, operations research, management science, decision science, and simulation modeling.
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