<p>I am the author of imputeTS, currently it is not possible to get multiple imputations using imputeTS.
(I have it in mind for further updates, but this will be a bigger effort, which certainly will take some time)</p>
<p>If you desperately need multiple imputations you can use the following workaround (without using imputeTS):</p>
<p>Use <strong>mice</strong> or another multiple imputation package for cross-sectional/non-time series data. Since you only have one variable or you don't have time information added to your dataset, you have to create additional time variables from your dataset.</p>
<p>So you add lags and leads, season, moving averages, ... as additional variables to your dataset. This way <strong>mice</strong> will also work for time series data and also give you multiple imputations. (see also <a href="https://arxiv.org/pdf/1510.03924.pdf" rel="nofollow noreferrer">this paper</a> from page 8 onward  ) But you need to be careful, you will get multiple imputations, but it might be, that the overall model isn't as good as when you use dedicated time series methods) With this approach you use ML methods for time series, but not every ML model is good for time series and your have to do a good job modeling you time series features into the additional variables you add to the dataset.</p>
Is multiple imputations possible in "imputeTS" package?
<p>This is about &quot;imputeTS&quot; package in R. I would like to know whether there is a way to do multiple imputations using this package?
Any guidance/directions about the possibilities of doing that would be greatly appreciated.</p>
<p>Also, I would like to know ideas about checking for the missing mechanism (MCAR, MAR, MNAR), particularly for a univariate time series.</p>