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cts: An R Package for Continuous Time Autoregressive Models via Kalman Filter

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  • Wang, Zhu

Abstract

We describe an R package cts for fitting a modified form of continuous time autoregressive model, which can be particularly useful with unequally sampled time series. The estimation is based on the application of the Kalman filter. The paper provides the methods and algorithms implemented in the package, including parameter estimation, spectral analysis, forecasting, model checking and Kalman smoothing. The package contains R functions which interface underlying Fortran routines. The package is applied to geophysical and medical data for illustration.

Suggested Citation

  • Wang, Zhu, 2013. "cts: An R Package for Continuous Time Autoregressive Models via Kalman Filter," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i05).
    • Handle: RePEc:jss:jstsof:v:053:i05
    DOI: http://hdl.handle.net/10.18637/jss.v053.i05
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    References listed on IDEAS

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    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    2. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, January.
    3. Zhu Wang & Wayne A. Woodward & Henry L. Gray, 2009. "The application of the Kalman filter to nonstationary time series through time deformation," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(5), pages 559-574, September.
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    Cited by:

    1. Stefano Iacus & Lorenzo Mercuri, 2015. "Implementation of Lévy CARMA model in Yuima package," Computational Statistics, Springer, vol. 30(4), pages 1111-1141, December.
    2. Driver, Charles C. & Oud, Johan H. L. & Voelkle, Manuel C., 2017. "Continuous Time Structural Equation Modeling with R Package ctsem," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i05).
    3. Andreia Monteiro & Raquel Menezes & Maria Eduarda Silva, 2021. "Modelling informative time points: an evolutionary process approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 364-382, June.
    4. Thieler, Anita M. & Fried, Roland & Rathjens, Jonathan, 2016. "RobPer: An R Package to Calculate Periodograms for Light Curves Based on Robust Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i09).
    5. Zhang, Shibin, 2020. "Nonparametric Bayesian inference for the spectral density based on irregularly spaced data," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).

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