Patent ID: 6363333
Filing Date: 2002-03-26
Classification: G06F

Abstract:
Method for the classification of the statistical dependence of a measurable, first time series that comprises a prescribable plurality of samples, particularly of an electrical signal, with a computer, comprising the steps of:1) neurally modelling a non-linear Markov process of the order m by training a neural network with data of the measurable, first time series to the prediction probabilities corresponding to the non-linear Markov process; 2) determining an arbitrary plurality of surrogates (Si, i=1, 2, . . . , M) that represent a second time series ({zt}, t=1, 2, . . . , N) in the neural network trained according to the non-linear Markov process; 3) calculating a respective criterion for statistical dependence d(r) for a plurality of r (r=1, 2, . . . ) for the first time series and the second time series, quantifying the statistical dependencies between the past of the respective time series and a value that lies r steps in the future for every individual surrogate (Si) as a difference to the measurable, first time series, and classifying said measurable, first time series dependent on said criterion.