I have a dataset of 380 samples of 6 variables. These variables are counts of different types of events in each of the 380 defined regions. These counts are per month, which means that I have several of these datasets (for now, I only have four months).
When looking at the data, I can clearly see that there is some missing (or incomplete) data. For instance, the counts for one given region are about the same for all months, except one (where it's close to zero). However, I does not seem likely that there were actually that few events during this period.
What I would like is to be able, given data for a few months, to detect missing or incomplete values, and possibly to correct/complete them. Detecting missing values is not as obvious as looking for zeros, because it may happen that no event occurred in some regions.
I read a few things about matrix factorization, but I'm not sure it would apply to my case. It seems suited for the cases where you know what data is missing.
I assume this kind of problem is be quite common, for instance in biology for population estimation.