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PARMA Models with Applications in R

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Cyclostationarity: Theory and Methods - II (CSTA 2014)

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Abstract

Periodic autoregressive–moving-average models (periodic ARMA models, PARMA models) are used to model non-stationary time series with periodic structure. They are similar to ARMA except the coefficients that are periodic in time with a common period. They are widely applied in climatology, hydrology, meteorology and economics data. In this paper we want to familiarize the readers with all the essential steps of PARMA model fitting. We present in detail the non-parametric spectral analysis, model identification, parameter estimation, diagnostic checking (model verification) and prediction on the real data example. Our aim is to provide appropriate tool for the complete analysis of periodic time series using PARMA modelling and to popularize this approach among non-specialists.

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Acknowledgments

Research of Anna E. Dudek was partially supported by the Polish Ministry of Science and Higher Education and AGH local grant.

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Correspondence to Anna E. Dudek .

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Dudek, A.E., Hurd, H., Wójtowicz, W. (2015). PARMA Models with Applications in R. In: Chaari, F., Leskow, J., Napolitano, A., Zimroz, R., Wylomanska, A., Dudek, A. (eds) Cyclostationarity: Theory and Methods - II. CSTA 2014. Applied Condition Monitoring, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-16330-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-16330-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16329-1

  • Online ISBN: 978-3-319-16330-7

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