I have time series consisting of 15 points of time, each containing around 15 values/features. Each time series is one sample, I have thousands of samples. Possible output is either 0 or 1, so binary classification.
Currently I feed in all 15x15 = 225 values = 225 inputs (per time series), without distinguishing or weighing them in time or any other aspect, in a normal backprop net (3 hidden layers, but less also works, Matlab Patternnet, scaled conjugate gradient) simultaneously and get best results (easily hits desired performance and gradient). Whenever I feed them in as vectors, e.g. 15 inputvectors with 15 values each, which would represent the time series way better, results get way worse. This is the first problem, I do not understand.
Another problem is, that with feeding them in just as 225 equal inputs, information about position in time series is not really contained. I hope I could use this information to increase performance of my neural net by reaching a higher level of abstraction and preventing overfitting.
When X is the input matrix for training and T the target Data, then it looks like this in case 1 (just put alls 225 values in equally):
X: 225x30000 double, T = 1x300000 double
In case 2 it would look like this:
X: 25x30000 cell with each cell containing 25x1 double, T= 1x30000 cell with each cell containing 1 double
Q1: Is case 2 the correct format as input? Q2: Should not it have the same result than case 1?
Why does that make such a big difference in performance?