I'm trying to predict high or low crime rate in municipalities (binary 1/0 response variable) using a range of socioeconomic variables. Im doing this with a panel dataset with 300 municipality over 17 years (2006-2016). To be more specific I train the model on data from 2006-2015 and then predict with data on features/predictors from 2016. The binary GAM I'm using for prediction has quite heavily autocorrelated residuals, how will this affect my predictions?
I have generally found very limited information on using panel/longitudinal data sets with binary response variables for prediction with Machine learning methods (Random Forest, Naive Bayes, K-NN) and would therefore also appreciate thoughts on this.
One thing that bugs me is how to make a model like random forest or GAM notice the id and time dimensions of a panel dataset.