  On the basis of a local-projective (LP) approach we develop a method of noise
reduction in time series that makes use of nonlinear constraints appearing due
to the deterministic character of the underlying dynamical system. The Delaunay
triangulation approach is used to find the optimal nearest neighboring points
in time series. The efficiency of our method is comparable to standard LP
methods but our method is more robust to the input parameter estimation.
  The approach has been successfully applied for separating a signal from noise
in the chaotic Henon and Lorenz models as well as for noisy experimental data
obtained from an electronic Chua circuit. The method works properly for a
mixture of additive and dynamical noise and can be used for the noise-level
detection.
