Geometric noise reduction for multivariate time series | Zendy

M. Eugenia Mera | Zendy; Manuel Morán | Zendy

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Geometric noise reduction for multivariate time series

Author(s) -

M. Eugenia Mera,

Manuel Morán

Publication year - 2006

Publication title -

chaos an interdisciplinary journal of nonlinear science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.971

H-Index - 113

eISSN - 1089-7682

pISSN - 1054-1500

DOI - 10.1063/1.2151159

Subject(s) - series (stratigraphy) , multivariate statistics , attractor , time series , mathematics , measure (data warehouse) , chaotic , noise (video) , reduction (mathematics) , noise reduction , statistics , algorithm , computer science , artificial intelligence , data mining , mathematical analysis , paleontology , geometry , image (mathematics) , biology

We propose an algorithm for the reduction of observational noise in chaotic multivariate time series. The algorithm is based on a maximum likelihood criterion, and its goal is to reduce the mean distance of the points of the cleaned time series to the attractor. We give evidence of the convergence of the empirical measure associated with the cleaned time series to the underlying invariant measure, implying the possibility to predict the long run behavior of the true dynamics.

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