Segmentation of non-stationary time series

1 August 1979

journal article
research article
Published by Taylor & Francis in International Journal of Systems Science

Vol. 10 (8) , 883-894
https://doi.org/10.1080/00207727908941629

Abstract

This paper discusses the statistics of measuring the difference between two spectral densities, to detect the change of amplitude and frequency in non-stationary time series. First, Kullback information is developed as a measure to segment non-stationary time series. It is shown here that Kullback information is equivalent to spectral matching error measure and the likelihood ratio of residuals of the autoregressive model and it is useful practically in the segmentation of non-stationary time series. Next, other measures such as Kullback divergence and Bhattacharyya distance are investigated to detect the change of amplitude and frequency in non-stationary time series.

Keywords

This publication has 9 references indexed in Scilit:

On the stationarity and normality of the electroencephalographic data during sleep stages
Computer Programs in Biomedicine, 1978
Feature extraction from the electroencephalogram by adaptive segmentation
Proceedings of the IEEE, 1977
Distance measures for speech processing
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1976
Linear Prediction of Speech
Published by Springer Nature ,1976
Linear Discriminant Functions for Stationary Time Series
Journal of the American Statistical Association, 1974
The Divergence and Bhattacharyya Distance Measures in Signal Selection
IEEE Transactions on Communications, 1967
Signal selection in communication and radar systems
IEEE Transactions on Information Theory, 1963
On the effectiveness of receptors in recognition systems
IEEE Transactions on Information Theory, 1963
Toeplitz Forms and Their Applications
Published by University of California Press ,1958