Abstract
We review Kalman filter and related smoothing methods for the continuous time state space model. The attractive property of continuous time state space models is that time gaps between consecutive observations in a time series are allowed to vary throughout the process. We discuss some essential details of the continuous time state space methodology and review the similarities and the differences between the continuous time and discrete time approaches. An application in the modelling of road traffic data is presented in order to illustrate the relevance of continuous time state space modelling in practice.
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The programming code of our analyses is available as supplementary material at the book website http://www.springer.com/us/book/9783319772189.
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Acknowledgements
We thank Rijkswaterstaat, The Netherlands (WVL), for providing us with the data set.
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Koopman, S.J., Commandeur, J.J.F., Bijleveld, F.D., Vujić, S. (2018). Continuous Time State Space Modelling with an Application to High-Frequency Road Traffic Data. In: van Montfort, K., Oud, J.H.L., Voelkle, M.C. (eds) Continuous Time Modeling in the Behavioral and Related Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-77219-6_13
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DOI: https://doi.org/10.1007/978-3-319-77219-6_13
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