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I am trying to model some historical variables which are discontinuous. I am working with monthly observations so I have 12 observations per year.

However, there are cases in which, for example, I have data for January-2000, February-2000, and then the data jumps to May-2000, and so on.

I am using R to perform the time series and forecasting analysis.

Is there a way to cope with this kind of irregular time series in R?

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  • $\begingroup$ Alternatively, you may be able to consider it a regularly-spaced time series with missing values. In that case, any state-space model which can be estimated through the Kalman filter algorithm will work automatically with missing values, and the algorithm will also provide non-anticipating estimates for the missing values, and forecasts. $\endgroup$
    – Chris Haug
    Commented Mar 23, 2017 at 17:53

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You could always try time series imputation: https://cran.r-project.org/web/packages/imputeTS/imputeTS.pdf

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