{"title":"Augmented Winter's method for forecasting under asynchronous seasonalities","authors":"O. Karabağ, Mehmet Murat Fadiloglu","doi":"10.1080/23270012.2020.1839362","DOIUrl":null,"url":null,"abstract":"Augmented Winter's method for forecasting under asynchronous seasonalities Oktay Karabağ & M. Murat Fadıloğlu To cite this article: Oktay Karabağ & M. Murat Fadıloğlu (2021) Augmented Winter's method for forecasting under asynchronous seasonalities, Journal of Management Analytics, 8:1, 19-35, DOI: 10.1080/23270012.2020.1839362 To link to this article: https://doi.org/10.1080/23270012.2020.1839362","PeriodicalId":46290,"journal":{"name":"Journal of Management Analytics","volume":" ","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23270012.2020.1839362","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Management Analytics","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1080/23270012.2020.1839362","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 2
Abstract
Augmented Winter's method for forecasting under asynchronous seasonalities Oktay Karabağ & M. Murat Fadıloğlu To cite this article: Oktay Karabağ & M. Murat Fadıloğlu (2021) Augmented Winter's method for forecasting under asynchronous seasonalities, Journal of Management Analytics, 8:1, 19-35, DOI: 10.1080/23270012.2020.1839362 To link to this article: https://doi.org/10.1080/23270012.2020.1839362
Oktay karabaku & M. Murat Fadıloğlu本文引用:Oktay karabaku & M. Murat Fadıloğlu(2021)基于非同步季节的Augmented Winter预测方法,管理分析学报,8:1,19-35,DOI: 10.1080/23270012.2020.1839362链接至本文:https://doi.org/10.1080/23270012.2020.1839362
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.