NOISE REDUCTION BASED ON DYNAMICS RECONSTRUCTION

A new technique of noise reduction in time series is presented. First, the data are embedded into higher dimensional state space.Then, the dynamics of the system is approximated and data are adjusted to satisfy better the approximations to the dynamics. It leads to noise reduction which is more effective than traditonal filtering, especially for complex time series.