Time series AR modeling with missing observations based on the polynomial transformation

Authors:
Jie Ding;Lili Han;Xiaoming Chen
Affiliations:
School of Communication and Control Engineering, Jiangnan University, Wuxi 214122, PR China;School of Communication and Control Engineering, Jiangnan University, Wuxi 214122, PR China;School of Communication and Control Engineering, Jiangnan University, Wuxi 214122, PR China
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2010

Citing 14
Cited 12

System identification (2nd ed.): theory for the user

System identification (2nd ed.): theory for the user
Performance analysis of multi-innovation gradient type identification methods

Automatica (Journal of IFAC)
The residual based extended least squares identification method for dual-rate systems

Computers & Mathematics with Applications
Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems

Computers & Mathematics with Applications
Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems

Automatica (Journal of IFAC)
Multi-innovation stochastic gradient algorithms for multi-input multi-output systems

Digital Signal Processing
Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises

Signal Processing
Performance analysis of estimation algorithms of nonstationary ARMA processes

IEEE Transactions on Signal Processing
Parameter Identification and Intersample Output Estimation for Dual-Rate Systems

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Combined parameter and output estimation of dual-rate systems using an auxiliary model

Automatica (Journal of IFAC)
Identification of Hammerstein nonlinear ARMAX systems

Automatica (Journal of IFAC)
Hierarchical gradient-based identification of multivariable discrete-time systems

Automatica (Journal of IFAC)
Auxiliary model identification method for multirate multi-input systems based on least squares

Mathematical and Computer Modelling: An International Journal
Inductive learning models with missing values

Mathematical and Computer Modelling: An International Journal

Multi-innovation generalized extended stochastic gradient algorithm for multi-input multi-output nonlinear Box-Jenkins systems based on the auxiliary model

LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Identification methods for Hammerstein nonlinear systems

Digital Signal Processing
Volterra system-based neural network modeling by particle swarm optimization approach

Neurocomputing
Two recursive least squares parameter estimation algorithms for multirate multiple-input systems by using the auxiliary model

Mathematics and Computers in Simulation
Least squares based iterative parameter estimation algorithm for multivariable controlled ARMA system modelling with finite measurement data

Mathematical and Computer Modelling: An International Journal
Auxiliary model based multi-innovation algorithms for multivariable nonlinear systems

Mathematical and Computer Modelling: An International Journal
Hierarchical least squares algorithms for single-input multiple-output systems based on the auxiliary model

Mathematical and Computer Modelling: An International Journal
Computing matrix functions using mixed interpolation methods

Mathematical and Computer Modelling: An International Journal
Signal frequency and parameter estimation for power systems using the hierarchical identification principle

Mathematical and Computer Modelling: An International Journal
Parameter estimation for nonlinear dynamical adjustment models

Mathematical and Computer Modelling: An International Journal
Differential evolution-based nonlinear system modeling using a bilinear series model

Applied Soft Computing
Decomposition based fast least squares algorithm for output error systems

Signal Processing

Quantified Score

Hi-index	0.98

Visualization

Abstract

This paper focuses on parameter estimation problems of auto-regression (AR) time series models with missing observations. The standard estimation algorithms cannot be applied to such AR models with missing observations. The polynomial transformation technique is employed to transform the AR models into models which can be identified from available scarce observations, then the extended stochastic gradient algorithm is proposed to fit the time series with missing observations. The convergence properties of the proposed algorithm are analyzed and an example is given to test and illustrate the conclusions in the paper.