Liudmyla Levenchuk, Oxana Tymoshchuk, Vira Huskova, Petro Bidyuk
{"title":"Uncertainties in data processing, forecasting and decision making","authors":"Liudmyla Levenchuk, Oxana Tymoshchuk, Vira Huskova, Petro Bidyuk","doi":"10.20535/srit.2308-8893.2023.3.05","DOIUrl":null,"url":null,"abstract":"Forecasting, dynamic planning, and current statistical data processing are defined as the process of estimating an enterprise’s current state on the market compared to other competing enterprises and determining further goals as well as sequences of actions and resources necessary for reaching the goals stated. In order to perform high-quality forecasting, it is proposed to identify and consider possible uncertainties associated with data and expert estimates. This is one of the system analysis principles to be hired for achieving high-quality final results. A review of some uncertainties is given, and an illustrative example showing improvement of the final result after considering possible stochastic uncertainty is provided.","PeriodicalId":30502,"journal":{"name":"Sistemni Doslidzena ta Informacijni Tehnologii","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sistemni Doslidzena ta Informacijni Tehnologii","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20535/srit.2308-8893.2023.3.05","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
Forecasting, dynamic planning, and current statistical data processing are defined as the process of estimating an enterprise’s current state on the market compared to other competing enterprises and determining further goals as well as sequences of actions and resources necessary for reaching the goals stated. In order to perform high-quality forecasting, it is proposed to identify and consider possible uncertainties associated with data and expert estimates. This is one of the system analysis principles to be hired for achieving high-quality final results. A review of some uncertainties is given, and an illustrative example showing improvement of the final result after considering possible stochastic uncertainty is provided.