# Using ARMA when data is missing

I am using ARMA over a dataset with missing samples. How do I treat them? Would you suggest to make linear/nonlinear interpolation or just keep them out and consider two samples with missing data in between as consecutive samples?

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There is no need to do anything. An ARMA model can easily be estimated with missing values within the time series. You need to use the state space representation of an ARMA model to compute the likelihood. If you use R, this is already handled automatically via the arima() function.