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Kenneth_afebu asked . 2021-07-06

ANN training using two time series as input

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

ann , multi-layer percep... , mutiple input-sing..., support vector mac...

Expert Answer

John Michell answered . 2024-05-17 19:01:22

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.


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