{"title":"基于时间相关自举数据的covid - 19元启发式优化预测方法","authors":"L. Fenga, Carlo Del Castello","doi":"10.1101/2020.04.02.20050153","DOIUrl":null,"url":null,"abstract":"A compounded method, exploiting the searching capabilities of an operation research algorithm and the power of bootstrap techniques, is presented. The resulting algorithm has been successfully tested to predict the turning point reached by the epidemic curve followed by the CoViD19 virus in Italy. Futures lines of research, which include the generalization of the method to a broad set of distribution, will be finally given.","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2020-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"CoViD19 Meta heuristic optimization based forecast method on time dependent bootstrapped data\",\"authors\":\"L. Fenga, Carlo Del Castello\",\"doi\":\"10.1101/2020.04.02.20050153\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A compounded method, exploiting the searching capabilities of an operation research algorithm and the power of bootstrap techniques, is presented. The resulting algorithm has been successfully tested to predict the turning point reached by the epidemic curve followed by the CoViD19 virus in Italy. Futures lines of research, which include the generalization of the method to a broad set of distribution, will be finally given.\",\"PeriodicalId\":44760,\"journal\":{\"name\":\"Journal of Probability and Statistics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2020-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Probability and Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2020.04.02.20050153\",\"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":"Journal of Probability and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2020.04.02.20050153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
CoViD19 Meta heuristic optimization based forecast method on time dependent bootstrapped data
A compounded method, exploiting the searching capabilities of an operation research algorithm and the power of bootstrap techniques, is presented. The resulting algorithm has been successfully tested to predict the turning point reached by the epidemic curve followed by the CoViD19 virus in Italy. Futures lines of research, which include the generalization of the method to a broad set of distribution, will be finally given.
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