{"title":"一种基于分割的时间序列数据相似性度量方法","authors":"Kakuli Mishra, Srinka Basu, U. Maulik","doi":"10.1145/3371158.3371221","DOIUrl":null,"url":null,"abstract":"Focusing on the objectives of Demand Side Management (DSM), we propose a novel time series distance metric that better capture the information related to similar peaks/off-peaks. The proposed metric uses autocorrelation based segmentation and similar segment identification for computation of overall distance. Experiment shows the proposed distance advances the state-of-the-art.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A segmentation based similarity measure for time series data\",\"authors\":\"Kakuli Mishra, Srinka Basu, U. Maulik\",\"doi\":\"10.1145/3371158.3371221\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Focusing on the objectives of Demand Side Management (DSM), we propose a novel time series distance metric that better capture the information related to similar peaks/off-peaks. The proposed metric uses autocorrelation based segmentation and similar segment identification for computation of overall distance. Experiment shows the proposed distance advances the state-of-the-art.\",\"PeriodicalId\":360747,\"journal\":{\"name\":\"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3371158.3371221\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3371158.3371221","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A segmentation based similarity measure for time series data
Focusing on the objectives of Demand Side Management (DSM), we propose a novel time series distance metric that better capture the information related to similar peaks/off-peaks. The proposed metric uses autocorrelation based segmentation and similar segment identification for computation of overall distance. Experiment shows the proposed distance advances the state-of-the-art.
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