{"title":"基于Hermite变换的非线性预测","authors":"T. McElroy, Srinjoy Das","doi":"10.1080/24754269.2020.1856589","DOIUrl":null,"url":null,"abstract":"ABSTRACT General prediction formulas involving Hermite polynomials are developed for time series expressed as a transformation of a Gaussian process. The prediction gains over linear predictors are examined numerically, demonstrating the improvement of nonlinear prediction.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"49 - 54"},"PeriodicalIF":0.7000,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2020.1856589","citationCount":"0","resultStr":"{\"title\":\"Nonlinear prediction via Hermite transformation\",\"authors\":\"T. McElroy, Srinjoy Das\",\"doi\":\"10.1080/24754269.2020.1856589\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT General prediction formulas involving Hermite polynomials are developed for time series expressed as a transformation of a Gaussian process. The prediction gains over linear predictors are examined numerically, demonstrating the improvement of nonlinear prediction.\",\"PeriodicalId\":22070,\"journal\":{\"name\":\"Statistical Theory and Related Fields\",\"volume\":\"5 1\",\"pages\":\"49 - 54\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2020-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/24754269.2020.1856589\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistical Theory and Related Fields\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1080/24754269.2020.1856589\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Theory and Related Fields","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1080/24754269.2020.1856589","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
ABSTRACT General prediction formulas involving Hermite polynomials are developed for time series expressed as a transformation of a Gaussian process. The prediction gains over linear predictors are examined numerically, demonstrating the improvement of nonlinear prediction.
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