About: Novel semi-metrics for multivariate change point analysis and anomaly detection

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Novel semi-metrics for multivariate change point analysis and anomaly detection Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Novel semi-metrics for multivariate change point analysis and anomaly detection
Creator	Chan, Jennifer James, Nick Menzies, Max Azizi, Lamiae
Source	ArXiv; Elsevier; Medline; PMC
abstract	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.
has issue date	2020-07-02(xsd:dateTime)
bibo:doi	10.1016/j.physd.2020.132636
bibo:pmid	32834249
has license	no-cc
sha1sum (hex)	7da900a9eb11eebc0bd4df3f0ad059fd0ac5bd41
schema:url	https://doi.org/10.1016/j.physd.2020.132636
resource representing a document's title	Novel semi-metrics for multivariate change point analysis and anomaly detection
has PubMed Central identifier	PMC7329734
has PubMed identifier	32834249
schema:publication	Physica D
resource representing a document's body	covid:7da900a9eb11eebc0bd4df3f0ad059fd0ac5bd41#body_text
is schema:about of	named entity 'APPLICATION' named entity 'TRIANGLE' named entity 'COMPUTATIONAL METHOD' named entity 'transitivity' named entity 'Hausdorff' named entity 'triangle inequality' named entity 'time series' named entity 'anomaly detection' named entity 'eigenvalues' named entity 'anomaly detection' named entity 'orthogonal matrix' named entity 'eigenvalues' named entity 'triangle inequality' named entity '1, 2' named entity '1.14' named entity 'Microsoft' named entity 'spectral theorem' named entity 'metric space' named entity 'distance matrix' named entity 'distance function' named entity 'time series' named entity 'dimensional space' named entity 'trade-off' named entity 'random variables' named entity 'heat map' named entity 'triangle inequality' named entity 'distance matrices' named entity 'applied sciences' named entity 'change points' named entity 'distance measure' named entity 'triangle inequality' named entity 'kurtosis' named entity 'time series' named entity 'Hausdorff' named entity 'triples' named entity 'C 1' named entity 'triangle inequality' named entity 'eigenvalues' named entity 'change points' named entity 'stochastic systems' named entity 'time series' named entity 'metric space' named entity 'triples' named entity 'change points' named entity 'triangle inequality' named entity 'distance matrix' named entity 'dendrogram' named entity 'COVID-19' named entity 'Hausdorff' named entity 'time series' named entity 'spectral clustering' named entity 'Type 1 error' named entity 'metric space' named entity 'Hausdorff distance' named entity 'Elsevier' named entity 'Change points' named entity 'Hausdorff metric' named entity 'distance matrix' named entity 'time series' named entity 'eigenvector' named entity 'mathematics' named entity 'triangle inequality' named entity 'probability measures' named entity 'triangle inequality' named entity 'time series' named entity 'Hausdorff' named entity 'spectral clustering' named entity 'time series' named entity 'geometric point' named entity 'change point detection' named entity 'data points' named entity 'triples' named entity 'data points' named entity 'image matching' named entity '1.14' named entity 'Hausdorff' named entity 'hierarchical clustering' named entity 'Hausdorff'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software