title: A method for comparing multivariate time series with different dimensions.
abstract: In 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.
authors: School of Computer Science and Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom.
authors: Avraam
authors: A
authors: Tapinos
authors: Pedro
authors: P
authors: Mendes
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D008962:Models, Theoretical
references: Circulation. 2000 Jun 13;101(23):E215-20
references: 10851218
references: IEEE Trans Biomed Eng. 2000 Sep;47(9):1185-94
references: 11008419
references: Bioinformatics. 2006 Dec 15;22(24):3067-74
references: 17032683
references: Nucleic Acids Res. 2006 Jan 1;34(Database issue):D689-91
references: 16381960
references: Sleep. 1995 Sep;18(7):557-64
references: 8552926
references: Bioinformatics. 2003 Mar 1;19(4):524-31
references: 12611808