I’ve seen this stated many times regarding training of narx networks and I need to understand more about the basis for the claim. At first the argument made sense so I used ‘divideind’ for narx training. But after further thought and some experimentation I’m not so sure.
When training a narxnet using the ‘dividerand’ data partitioning (net.divideFcn = 'dividerand'), does the Matlab code actually randomly parse the data into separate training, validation & testing datasets for independent narxnet calculations? Or does the Matlab code preserve the time sequence of all the inputs & targets and simply mask the irrelevant data partitions before computing the performance statistics for each partition?
If the latter, then I don’t see how ‘dividerand’ would destroy the serial correlations.
I’ve not seen anything in the NN Toolbox documentation warning users to avoid using ‘dividerand’ for narxnets. If anyone knows the code well or has done some testing to confirm, please advise! I suspect this topic is of interest to many.