{"title":"AutoLag:自动发现流数据中的滞后相关性","authors":"Yasushi Sakurai, S. Papadimitriou, C. Faloutsos","doi":"10.1109/ICDE.2005.24","DOIUrl":null,"url":null,"abstract":"We have introduced the problem of automatic lag correlation detection on streaming data and proposed AutoLag to address this problem by using careful approximations and smoothing. Our experiments on real and realistic data show that AutoLag works as expected, estimating the unknown lags with excellent accuracy and significant speed-up. In our experiments on real and realistic data, AutoLag was up to about 42,000 times faster than the naive implementation, with at most 1% relative error.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"AutoLag: automatic discovery of lag correlations in stream data\",\"authors\":\"Yasushi Sakurai, S. Papadimitriou, C. Faloutsos\",\"doi\":\"10.1109/ICDE.2005.24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We have introduced the problem of automatic lag correlation detection on streaming data and proposed AutoLag to address this problem by using careful approximations and smoothing. Our experiments on real and realistic data show that AutoLag works as expected, estimating the unknown lags with excellent accuracy and significant speed-up. In our experiments on real and realistic data, AutoLag was up to about 42,000 times faster than the naive implementation, with at most 1% relative error.\",\"PeriodicalId\":297231,\"journal\":{\"name\":\"21st International Conference on Data Engineering (ICDE'05)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"21st International Conference on Data Engineering (ICDE'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.2005.24\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"21st International Conference on Data Engineering (ICDE'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2005.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AutoLag: automatic discovery of lag correlations in stream data
We have introduced the problem of automatic lag correlation detection on streaming data and proposed AutoLag to address this problem by using careful approximations and smoothing. Our experiments on real and realistic data show that AutoLag works as expected, estimating the unknown lags with excellent accuracy and significant speed-up. In our experiments on real and realistic data, AutoLag was up to about 42,000 times faster than the naive implementation, with at most 1% relative error.