{"title":"基于hj双标图的交通矩阵时空分析方案","authors":"Francisco Javier Delgado Alvarez, P. G. Villardon","doi":"10.1109/IWMN.2015.7322979","DOIUrl":null,"url":null,"abstract":"Since 2002 many works have been published applying Principal Component Analysis (PCA) in the study of network traffic. These investigations have revealed some issues inherent to the temporal and spatial correlations present in the data, which are not considered in PCA. The proposed solutions to these problems include the formulation of a new matrix of data “rearranged” to take these effects into consideration. Nevertheless the “classical” Biplot methods (GH, JK, HJ) reflect both correlations between time intervals and between time series. The Biplot methods provide graphical procedures that offer more information about the behavior of time series than PCA, while making possible the use of more specific quality of representation metrics. In the case of HJ-Biplot, the graphical display obtained has maximum quality of representation for rows and columns.","PeriodicalId":440636,"journal":{"name":"2015 IEEE International Workshop on Measurements & Networking (M&N)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A proposal for spatio-temporal analysis of traffic matrices using HJ-biplot\",\"authors\":\"Francisco Javier Delgado Alvarez, P. G. Villardon\",\"doi\":\"10.1109/IWMN.2015.7322979\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since 2002 many works have been published applying Principal Component Analysis (PCA) in the study of network traffic. These investigations have revealed some issues inherent to the temporal and spatial correlations present in the data, which are not considered in PCA. The proposed solutions to these problems include the formulation of a new matrix of data “rearranged” to take these effects into consideration. Nevertheless the “classical” Biplot methods (GH, JK, HJ) reflect both correlations between time intervals and between time series. The Biplot methods provide graphical procedures that offer more information about the behavior of time series than PCA, while making possible the use of more specific quality of representation metrics. In the case of HJ-Biplot, the graphical display obtained has maximum quality of representation for rows and columns.\",\"PeriodicalId\":440636,\"journal\":{\"name\":\"2015 IEEE International Workshop on Measurements & Networking (M&N)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Workshop on Measurements & Networking (M&N)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWMN.2015.7322979\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Workshop on Measurements & Networking (M&N)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWMN.2015.7322979","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A proposal for spatio-temporal analysis of traffic matrices using HJ-biplot
Since 2002 many works have been published applying Principal Component Analysis (PCA) in the study of network traffic. These investigations have revealed some issues inherent to the temporal and spatial correlations present in the data, which are not considered in PCA. The proposed solutions to these problems include the formulation of a new matrix of data “rearranged” to take these effects into consideration. Nevertheless the “classical” Biplot methods (GH, JK, HJ) reflect both correlations between time intervals and between time series. The Biplot methods provide graphical procedures that offer more information about the behavior of time series than PCA, while making possible the use of more specific quality of representation metrics. In the case of HJ-Biplot, the graphical display obtained has maximum quality of representation for rows and columns.