A Method for Comparing Multivariate Time Series with Different Dimensions

dc.contributor.authorTapinos, Avraamen
dc.contributor.authorMendes, Pedroen
dc.date.accessioned2018-10-23T17:18:39Zen
dc.date.available2018-10-23T17:18:39Zen
dc.date.issued2013-02-05en
dc.description.abstractIn many situations it is desirable to compare dynamical systems based on their behavior. Similarity of behavior often implies similarity of internal mechanisms or dependency on common extrinsic factors. While there are widely used methods for comparing univariate time series, most dynamical systems are characterized by multivariate time series. Yet, comparison of multivariate time series has been limited to cases where they share a common dimensionality. A semi-metric is a distance function that has the properties of non-negativity, symmetry and reflexivity, but not sub-additivity. Here we develop a semi-metric – SMETS – that can be used for comparing groups of time series that may have different dimensions. To demonstrate its utility, the method is applied to dynamic models of biochemical networks and to portfolios of shares. The former is an example of a case where the dependencies between system variables are known, while in the latter the system is treated (and behaves) as a black box.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0054201en
dc.identifier.eissn1932-6203en
dc.identifier.issue2en
dc.identifier.othere54201en
dc.identifier.pmid23393554en
dc.identifier.urihttp://hdl.handle.net/10919/85464en
dc.identifier.volume8en
dc.language.isoenen
dc.publisherPLOSen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleA Method for Comparing Multivariate Time Series with Different Dimensionsen
dc.title.serialPLOS ONEen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
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