RT Journal Article
T1 Geometric noise reduction for multivariate time series
A1 Mera Rivas, María Eugenia
A1 Morán Cabré, Manuel
AB 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.
PB American Institute of Physics
SN 1054-1500
YR 2006
FD 2006
LK https://hdl.handle.net/20.500.14352/52381
UL https://hdl.handle.net/20.500.14352/52381
LA eng
DS Docta Complutense
RD 1 may 2024