Automatic segmentation of stochastic time series using a whitening filter

Abstract

The problem of automatic segmentation of a stochastic time series into homogeneous data segments one cluster long is posed and solved according to the general formulation of the disorder problem. A new algorithm with cluster normalization by the variance of the generating noise is developed using an autoregression model and the minimum information divergence criterion. It is shown that the main advantage of the proposed algorithm over existing analogs is high dynamic properties. The algorithm was tested by analyzing stock market dynamics in the United States and Russia. Estimates of the admissible (threshold) level of disorder in a time series within one cluster are obtained using the Kullback-Leibler information measure.