NDLI: Automatic segmentation of stochastic time series using a whitening filter

Content Provider	Springer Nature Link
Author	Savchenko, V. V. Pomarev, D. A.
Copyright Year	2009
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.
Starting Page	37
Ending Page	42
Page Count	6
File Format	PDF
ISSN	87566990
Journal	Optoelectronics, Instrumentation and Data Processing
Volume Number	45
Issue Number	1
e-ISSN	19347944
Language	English
Publisher	Allerton Press, Inc.
Publisher Date	2009-04-06
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	stochastic time series autoregression model forecast estimate whitening filter minimum information divergence criterion Applied Optics, Optoelectronics, Optical Devices
Content Type	Text
Resource Type	Article
Subject	Instrumentation Condensed Matter Physics Electrical and Electronic Engineering

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Automatic segmentation of stochastic time series using a whitening filter