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10.1016/j.physd.2020.132636 http://scihub22266oqcxt.onion/10.1016/j.physd.2020.132636 C7329734!7329734!32834249     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Physica+D 2020 ; 412 (ä): 132636 Nephropedia Template TP {{tp|p=32834249|t=2020. Novel semi-metrics for multivariate change point analysis and anomaly detection |pdf=|usr=}}{{32834249}} gab.com Text Novel semi-metrics for multivariate change point analysis and anomaly detection JO: Physica D http://www.kidney.de/pget.php?&tw=1&pmid=32834249 #FOAvip This paper proposes a new method for determining similarity and anomalies between time series, most practically effective in large collections of (likely related) time series, by measuring distances between structural breaks within such a collection. We introduce a class of semi-metric distance measures, which we term MJ distances. These semi-metrics provide an advantage over existing options such as the Hausdorff and Wasserstein metrics. We prove they have desirable properties, including better sensitivity to outliers, while experiments on simulated data demonstrate that they uncover similarity within collections of time series more effectively. Semi-metrics carry a potential disadvantage: without the triangle inequality, they may not satisfy a ?transitivity property of closeness.? We analyse this failure with proof and introduce an computational method to investigate, in which we demonstrate that our semi-metrics violate transitivity infrequently and mildly. Finally, we apply our methods to cryptocurrency and measles data, introducing a judicious application of eigenvalue analysis. Twit Text FOAVip Novel semi-metrics for multivariate change point analysis and anomaly detection ????Physica D http://www.kidney.de/pget.php?&tw=1&pmid=32834249 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32834249 #FOAvip English Wikipedia <ref>{{Cite pmid|32834249}}</ref> Novel semi-metrics for multivariate change point analysis and anomaly detection #MMPMID32834249 James N; Menzies M; Azizi L; Chan J Physica D 2020[Nov]; 412 (ä): 132636 PMID32834249show ga This paper proposes a new method for determining similarity and anomalies between time series, most practically effective in large collections of (likely related) time series, by measuring distances between structural breaks within such a collection. We introduce a class of semi-metric distance measures, which we term MJ distances. These semi-metrics provide an advantage over existing options such as the Hausdorff and Wasserstein metrics. We prove they have desirable properties, including better sensitivity to outliers, while experiments on simulated data demonstrate that they uncover similarity within collections of time series more effectively. Semi-metrics carry a potential disadvantage: without the triangle inequality, they may not satisfy a ?transitivity property of closeness.? We analyse this failure with proof and introduce an computational method to investigate, in which we demonstrate that our semi-metrics violate transitivity infrequently and mildly. Finally, we apply our methods to cryptocurrency and measles data, introducing a judicious application of eigenvalue analysis. äNovel semi-metrics for multivariate change point analysis and anomaly (epmc).pdf DeepDyve Pubget Overpricing