Change detection in nonstationary time series in linear regression framework

Title:

Change detection in nonstationary time series in linear regression framework

Creator:

Popescu, Theodor D.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:3c31f4b1-d179-4e09-9533-2a06b6df75c4
uuid:3c31f4b1-d179-4e09-9533-2a06b6df75c4

Subject:

change detection, segmentation, statistical hypothesis testing, linear regression models, and nonstationary time series

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The problem of change detection in nonstatioriary time series using linear regression models is addressed. It is assumed that the data can by accurately described by a linear regression model with piece-wise constant parameters. Due to the limitations of some classical approaches, based upon the innovation of one autoregressive (AR) model, most algorithms for the change detection presented make use of two AR models: one is a reference model, and the other one is a current model updated via a sliding block. Changes are detected when a suitable “distance” between these two models is high. Three “distance” measures are considered in the paper: cepstral distance, log-likelihood ratio (justified by GLR) and a distance involving the cross-entropy of the two conditional probabilities laws (divergence test). Other methods based on the quadratic forms of Gaussian random variables are also discussed in the paper. Finally, a change detection algorithm using three models and the evolution of Akaike Information Criterion is presented. All the presented algorithms constituted the object of evaluation by multiple simulation and háve been used to change detection in some nonstationary financial and economic time series.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2003 Volume:13 | Number:2

Harvested from:

CDK

Metadata only:

false