ANN training using two time series as input

Question

Hello everyone. A) Please and I need help on how I can train a Neural network using two sets of time series (same size) with each being row vectors ranging up to 1500 data points (1x1500) as input variable and to return a single class of output. Input 1= [-20 -20.30 -20.61 -20.91 -21.20 -21.49 -21.77 -22.05....] Input 2= [-15.81 -15.44 -15.05 -14.67 -14.28 -13.88 -13.48 -13.08....] Output=[H1] I have about four classes of output that each case of the double time series can define and I have about 700 cases of the above data type that I want to use for training, validation and testing. B) In another case I also want repeat the above procedure but my output will be two vectors i.e something like: Input 1= [-20 -20.30 -20.61 -20.91 -21.20 -21.49 -21.77 -22.05....] Input 2= [-15.81 -15.44 -15.05 -14.67 -14.28 -13.88 -13.48 -13.08....] Output= [0.2331 -3.221] A typical sample of the code for training and testing using MATLAB functions will be appreciated Please I am new to this area of study, simple and easily understandable terms will be preferred

John Michell · Accepted Answer

You have a mischaracterization of the standard classification and timeseries models.
 
1. Classification input data is divided into class-labeled areas of I-dimensional input space. However, the areas of a single class are not necessarily connected.
 
 
   The input matrix for c classes contains N   
I-dimensional samples and has dimension [ I N ].

   The output matrix for c classes contains N 
c-dimensional 0-1 unit vectors where the row of 
the 1 indicates the class index of the  
corresponding class.

   In general there is no correlation between 
the physical location of inputs and their matrix 
location.
2. Typically, time-series are not used for classification. Instead they consist of a series of correlated inputs that so that , for example, a string of m samples are used to estimate the following sample of the same series and/or an associated output series.

ANN training using two time series as input

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