Why do I need to run a model on multiple imputed datasets? In R, there is a package called mice which multiply imputes a dataset.
For my situation, I imputed using the predictive mean matching (pmm) method using a time series that I acquired that details multiple different climate variables like average temperature, relative humidity, precipitation etc.
At some point I would like to combine the results of these multiply imputed datasets, but in the syntax of the mice packages in R I apparently need to fit a statistical model on each imputed dataset.
But why do I need this? I mean sure, I need to run a model on the completed dataset (artificial neural networks), but that comes after. 
Examples on the internet use the lm() package, but that's not exactly what I need.
What should I do and why should I do it?
Example on the internet: https://datascienceplus.com/imputing-missing-data-with-r-mice-package/
Thanks to anyone who replies.
 A: The imputed values on your datasets obtained through multiple imputation are predictions from statistical models themselves, and vary according to probabilistic distributions as any predictions from statistical models would. 
The problem with using them as if they were observed data is that you will underestimate the variances and the covariances of the estimations in your model because the model doesn't account for the variance coming from your imputed variables being predictions.
Estimating your model several times across multiple imputed datasets and then combining the estimates of parameters and standard errors through a set of rules (e.g. the ones proposed by Rubin) allows you to reintroduce that variability in your models and avoid overestimating things like test statistics.
If you treat your imputed datasets as if they were observed data, you run the risk of underestimating standard errors and things that shouldn't be statistically significant will show up as being significant. This is why you don't do it.
