# Considerations for time series imputation

I have some data which is recordings of noise levels within a cellular network, noise value vs. time. In the middle of the night the levels return to normal operating levels which are typically -105dBm while during the day these values fluctuate depending upon the number of users and how much data they are pushing/pulling over the air, values can reach -90dBm during busy hour (4-7pm).

In my recordings I have some samples that are missing, they were either never recorded or there was some intervention on the site and there was no data recorded while the site was off air.

If I wish to impute this missing data what considerations do i need to factor in? There is a time series imputation package in R with a variety of methods but I am unsure as to which method is most appropriate. Any pointers would be greatly appreciated.

For example, with the function: na.interpolation(time_series, option = "stine"), you will obtain the time series with Stineman interpolation where there are missing values.
If you want something more immediate you can use the function: na.mean(x, option = "median"), so that the missing value are replaced with the median one.