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Time Series Modeling for Analysis and ControlTime Series Analysis Through AR Modeling

Time Series Modeling for Analysis and Control: Time Series Analysis Through AR Modeling [The features of dynamic phenomena can be described using time series models. In this chapter, we present various types of autoregressive models for the analysis of time series, such as univariate and multivariate autoregressive models, an autoregressive model with exogenous variables, a locally stationary autoregressive model, and a radial basis function autoregressive model. Various tools for analyzing dynamic systems such as the impulse response function, the power spectrum, the characteristic roots, and the power contribution are obtained through these models (Akaike and Nakagawa 1989; Kitagawa 2010).] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Time Series Modeling for Analysis and ControlTime Series Analysis Through AR Modeling

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Publisher
Springer Japan
Copyright
© The Author(s) 2015
ISBN
978-4-431-55302-1
Pages
7 –56
DOI
10.1007/978-4-431-55303-8_2
Publisher site
See Chapter on Publisher Site

Abstract

[The features of dynamic phenomena can be described using time series models. In this chapter, we present various types of autoregressive models for the analysis of time series, such as univariate and multivariate autoregressive models, an autoregressive model with exogenous variables, a locally stationary autoregressive model, and a radial basis function autoregressive model. Various tools for analyzing dynamic systems such as the impulse response function, the power spectrum, the characteristic roots, and the power contribution are obtained through these models (Akaike and Nakagawa 1989; Kitagawa 2010).]

Published: Mar 20, 2015

Keywords: AR(X) modeling; Ship motion analysis; LSAR model; Ship motion monitoring; RBF-ARX modeling for nonlinear system

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