Imputation data is a large area of research and complications. One must be very careful.
In this specific case, you need to understand if:
1) Are the missing values random?
If so, we can try some basic predictive model (such as a non-temporal regression) to try to fill in the null values. If they are not random, as is most common, you must understand the fact that the missing is generated (such as replacing zero values with the absence of information that obviously has different meanings)
2) Are the intervals between missing values regular?
If they are regular you will have a "seasonal effect" in your autocorrelated series (either in time, space or other dimension) and this will make it difficult to fill in, perhaps an alternative in this case is a interpolation of non-missing results. If this range is extremely rigid (for example, you never have data on Fridays), one alternative is to drop every Friday from the model and predict without that data (taking care not to skew the prediction)
An alternative approach would be to use machine learning models that treat autocorrelated data and pass a "warning" variable that in that field value is missing, for example an LSTM. With the disadvantage that you will lose interpretability of the model (as a SARIMA would do)