The theory of KM2O-Langevin equations and its applications to data analysis (III): Deterministic analysis

Abstract

Abstract.A technique is given for detecting deterministic dynamics in time series. Some stochastic difference equations, called KM2O-Langevin equations, are extracted directly from given data. We can find deterministic dynamics in time series by evaluating the magnitude of innovation part of the KM2O-Langevin equations. We can further find chaotic dynamics in time series by predicting it from the viewpoint of the theory of KM2O-Langevin equations.We apply our method to the data of measles and chicken pox, which are also treated by G. Sugihara and R.M. May in [1]. The result of numerical experiments indicates that there seem to exist some deterministic dynamics in both time series. It also suggests, however, that the data of measles seems to be chaotic while that of chicken pox not, which corresponds to the result of G. Sugihara and R.M. May.