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).
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Ohtsu, K., Peng, H., Kitagawa, G. (2015). Time Series Analysis Through AR Modeling. In: Time Series Modeling for Analysis and Control. SpringerBriefs in Statistics(). Springer, Tokyo. https://doi.org/10.1007/978-4-431-55303-8_2
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