Question

My task is to classify time-series data with use of MATLAB and any neural-network framework.

Describing task more specifically:
Is is a problem from computer-vision field. Is is a scene boundary detection task.

Source data are 4 arrays of neighbouring frame histogram correlations from the videoflow.
Based on this data, we have to classify this timeseries with 2 classes:

• “scene break”
• “no scene break”

So network input is 4 double values for each source data entry, and output is one binary value. I am going to show example of src data below:

0.997894,0.999413,0.982098,0.992164
0.998964,0.999986,0.999127,0.982068
0.993807,0.998823,0.994008,0.994299
0.225917,0.000000,0.407494,0.400424
0.881150,0.999427,0.949031,0.994918

Problem is that pattern-recogition tools from Matlab Neural Toolbox (like patternnet) threat source data like independant entrues. But I have strong belief that results will be precise only if net take decision based on the history of previous correlations.

But I also did not manage to get valid response from reccurent nets which serve time series analysis (like delaynet and narxnet).

narxnet and delaynet return lousy result and it looks like these types of networks not supposed to solve classification tasks. I am not insert any code here while it is allmost totally autogenerated with use of Matlab Neural Toolbox GUI.

I would apprecite any help. Especially, some advice which tool fits better for accomplishing my task.

Answer 1

I am not sure how difficult to classify this problem.
Given your sample, 4 input and 1 output feed-forward neural network is sufficient.

If you insist on using historical inputs, you simply pre-process your input d, such that

Your new input D(t) (a vector at time t) is composed of d(t) is a 1×4 vector at time t; d(t-1) is 1×4 vector at time t-1;… and d(t-k) is a 1×4 vector at time t-k.

If t-k <0, just treat it as ‘0’.

So you have a 1x(4(k+1)) vector as input, and 1 output.
Similar as Dan mentioned, you need to find a good k.

Speaking of the weights, I think additional pre-processing like windowing method on the input is not necessary, since neural network would be trained to assign weights to each input dimension.

It sounds a bit messy, since the neural network would consider each input dimension independently. That means you lose the information as four neighboring correlations.

One possible solution is the pre-processing extracts the neighborhood features, e.g. using mean and std as two features representative for the originals.