I'm working with Neural Toolbox using a dynamic network with time series data. Data consist on several contiguous data blocks (i.e. multiple sequences). Because there aren't so many large continous blocks, I thought I could make use of 'catsample' function to create a concurrent set of sequences, like described here: http://www.mathworks.es/help/toolbox/nnet/ug/bss36ff-1.html
The problem I'm encountering is related to the division of such data. I want to make a manual division into training, validation and testing data sets. So reviewing the data division functions avalaible on neural toolbox I chose the 'divideind' as the most suitable, but time series data are sequential data and therefore it's a cell array of double matrices which in turn each one has a concurrent set of sequences... So I can't imagine how to set up the indices for each data set. Because If I set for example:
[TrainInd, ValInd, TestInd] = divideind(nnInputData, 1:10, 11:20, 21:30);
I get TrainInd with all the sequences and ValInd and TestInd with nothing (just empty )
Could you tell me some idea please?