{"title":"使用具有阻尼趋势和季节成分的稳健指数平滑预测","authors":"Ruben Crevits, C. Croux","doi":"10.2139/ssrn.3068634","DOIUrl":null,"url":null,"abstract":"We provide a framework for robust exponential smoothing. For a class of exponential smoothing variants, we present a robust alternative. The class includes models with a damped trend and/or seasonal components. We provide robust forecasting equations, robust starting values, robust smoothing parameter estimation and a robust information criterion. The method is implemented in the R package robets, allowing for automatic forecasting. We compare the standard non-robust version with the robust alternative in a simulation study. Finally, the methodology is tested on data.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Forecasting Using Robust Exponential Smoothing with Damped Trend and Seasonal Components\",\"authors\":\"Ruben Crevits, C. Croux\",\"doi\":\"10.2139/ssrn.3068634\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We provide a framework for robust exponential smoothing. For a class of exponential smoothing variants, we present a robust alternative. The class includes models with a damped trend and/or seasonal components. We provide robust forecasting equations, robust starting values, robust smoothing parameter estimation and a robust information criterion. The method is implemented in the R package robets, allowing for automatic forecasting. We compare the standard non-robust version with the robust alternative in a simulation study. Finally, the methodology is tested on data.\",\"PeriodicalId\":170198,\"journal\":{\"name\":\"ERN: Forecasting Techniques (Topic)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Forecasting Techniques (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3068634\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Forecasting Techniques (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3068634","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Forecasting Using Robust Exponential Smoothing with Damped Trend and Seasonal Components
We provide a framework for robust exponential smoothing. For a class of exponential smoothing variants, we present a robust alternative. The class includes models with a damped trend and/or seasonal components. We provide robust forecasting equations, robust starting values, robust smoothing parameter estimation and a robust information criterion. The method is implemented in the R package robets, allowing for automatic forecasting. We compare the standard non-robust version with the robust alternative in a simulation study. Finally, the methodology is tested on data.