Note: Question edited in order to focus on the subject.
I'm using neural networks with 5 input time series of 3000 samples, to model 1 output time serie of 3000 samples. To do so, I used code generated thanks to the Neural Network toolbox for Time Series, and adapted it by using layrecnet. I use a 5x3000 matrix and a 1x3000 matrix to generate X (1x3000 cells of 5x1 double) and T (1x3000 cells of 1x1 double) with tonndata function followed by preparets before training. My network looks like this:
In network properties, it has only 1 input, which is a 2D dimensional input (since I have 5x1 double in each cell). I found this topic which explains how to use multiple inputs for a feed forward network. I then generated this 5 inputs network:
The only way I've been able to use this of network is by transforming by input_data into a 5x3000 cells and target into a 1x3000 cells. Which seems to be working fine, but training seems different from the previous one, with more frequent exit based on Mu threshold.
>> In my scenario, what are the pratical differences between the two pictured RNNS ?
I understand that second solutions allows for more freedom for each input (maybe delays, or the possibility to feed inputs to different layers), but is this useful in my scenario ?
Thank you in advance.