Publisher Summary This chapter starts with a brief introductory review of some of the recent developments of time-series analysis One of the most established procedures of time-series analysis is the method of estimation of power spectrum through windowed sample covariance sequence Except for the special situations where the orders are specified in advance, any method of the autoregressive model fitting is not well-defined as a method of estimation of the covariance sequence in the absence of the description of the rules for the determination of the order Because the covariance sequence determines the power spectrum, once an appropriate rule for the order determination is given, the autoregressive-model fitting procedure automatically provides an estimate of the power spectrum A solution to the problem of order determination of an auto-regressive model was obtained in 1969 by using the concept of final prediction error (FPE), which is defined as the one-step ahead prediction error variance when the least squares estimates of the autoregressive coefficients are used for prediction The concept of the one-step ahead of prediction error variance had difficulty in extending the minimum FPE procedure to the multivariate situation because of the non-uniqueness of the measure of variance in the multivariate situation A solution was found with the aid of the Gaussian model and the concept of maximum likelihood, which suggested the use of the generalized variance of the one-step ahead of prediction error

Application of System Identification Techniques to the Determination of Ship Dynamics

Canonical Correlation Analysis of Time Series and the Use of an Information Criterion

Parameter estimation for continuous-time models-A survey

Special section system identification tutorial: Maximum likelihood and prediction error methods

https://portal.research.lu.se/portal/files/9954705/ast_kall76.pdf

Identification of ship steering dynamics

Publisher Summary The problem of identification is to determine a model that describes input–output data obtained from a certain system. In this chapter, strong consistency for general prediction error methods, including the maximum-likelihood (ML) method is considered. The results are valid for general process models: linear and nonlinear. An error identification method is discussed in the chapter along with a general model for stochastic dynamic systems. Different identifiability concepts are also introduced, where a procedure to prove consistency is outlined. C onsistency is shown for a general system structure, as well as for linear systems. The application of the results to linear time-invariant systems is also discussed in the chapter.

/pdf/on-the-consistency-of-prediction-error-identification-44x3i6ptya.pdf

Application of System Identification Techniques to the Determination of Ship Dynamics

Citations

Canonical Correlation Analysis of Time Series and the Use of an Information Criterion

Parameter estimation for continuous-time models-A survey

Special section system identification tutorial: Maximum likelihood and prediction error methods

Identification of ship steering dynamics

On The Consistency of Prediction Error Identification Methods

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