A Recursive Algorithm for Adaptive Estimation and Parameter Change Detection of Time Series Models

1986 ◽  
Vol 37 (10) ◽  
pp. 987
Author(s):  
Tep Sastri
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Adaptive Estimation ◽  
Recursive Algorithm ◽  
Time Series Models ◽  
Parameter Change

Structural Identification Using Order Overspecified Time-Series Models

1992 ◽  
Vol 114 (1) ◽  
pp. 27-33 ◽  
Author(s):  
J. J. Hollkamp ◽  
S. M. Batill
Keyword(s):  
Time Series ◽  
Recursive Algorithm ◽  
Time Series Models ◽  
Minimum Norm ◽  
Minimum Norm Solution ◽  
Model Order ◽  
Identification Process ◽  
Truncated Svd ◽  
Value Decomposition ◽  
Norm Approximation

An identification method that uses order overspecified time-series models and a truncated singular value decomposition (SVD) solution is studied. The overspecified model reduces the effects of noise during the identification process, but produces extraneous modes. A backwards approach coupled with a minimum norm approximation, using a truncated SVD solution, enables the system modes to be distinguished from the extraneous modes of the model. Experimental data from a large flexible truss is used to study the effects of varying the truncation of the SVD solution and an order recursive algorithm is used to study the effects of model order. Results show that the SVD may be ineffective in separating the data into signal and noise subspaces. However solutions for highly overspecified model orders exhibit solution properties similar to the minimum norm solution and system and computational modes can be discriminated without a truncated solution.

Some Aspects of Asymptotic Theory with Applications to Time Series Models

1995 ◽  
Vol 11 (5) ◽  
pp. 818-887 ◽  
Author(s):  
P. Jeganathan
Keyword(s):  
New York ◽  
Time Series ◽  
Linear Time ◽  
Adaptive Estimation ◽  
Nonlinear Models ◽  
Unit Roots ◽  
Recent Book ◽  
Time Series Models ◽  
Likelihood Ratios ◽  
Time Series Regression

The primary purpose of this paper is to review a very few results on some basic elements of large sample theory in a restricted structural framework, as described in detail in the recent book by LeCam and Yang (1990, Asymptotics in Statistics: Some Basic Concepts. New York: Springer), and to illustrate how the asymptotic inference problems associated with a wide variety of time series regression models fit into such a structural framework. The models illustrated include many linear time series models, including cointegrated models and autoregressive models with unit roots that are of wide current interest. The general treatment also includes nonlinear models, including what have become known as ARCH models. The possibility of replacing the density of the error variables of such models by an estimate of it (adaptive estimation) based on the observations is also considered.Under the framework in which the asymptotic problems are treated, only the approximating structure of the likelihood ratios of the observations, together with auxiliary estimates of the parameters, will be required. Such approximating structures are available under quite general assumptions, such as that the Fisher information of the common density of the error variables is finite and nonsingular, and the more specific assumptions, such as Gaussianity, are not required. In addition, the construction and the form of inference procedures will not involve any additional complications in the non-Gaussian situations because the approximating quadratic structure actually will reduce the problems to the situations similar to those involved in the Gaussian cases.

Monitoring parameter change in time series models

2011 ◽  
Vol 20 (2) ◽  
pp. 171-199 ◽  
Author(s):  
Okyoung Na ◽  
Youngmi Lee ◽  
Sangyeol Lee
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Parameter Change ◽  
Monitoring Parameter

Retrospective change detection for binary time series models

2014 ◽  
Vol 145 ◽  
pp. 102-112 ◽  
Author(s):  
Konstantinos Fokianos ◽  
Edit Gombay ◽  
Abdulkadir Hussein
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Time Series Models ◽  
Binary Time Series
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