Prashant Kumar answered . 2024-05-17 18:52:39

 x(T-N) ... x(T-1)

    % Target Vector

    T = [3 9 12 4 10 13 5]; 

    % Input Vector

    delaymat = toeplitz([3 0],T);

    P = delaymat(2,:);

    % Number of hidden neurons

    S1 = 20;

    % Create Network

    net = newelm(P,T,S1);

    % Train Network

    Pseq = con2seq(P);

    Tseq = con2seq(T);

    net = train(net,Pseq,Tseq);

    % Test the network 

    Y = sim(net,Pseq);

    z = seq2con(Y);

    diff1 = T - z{1,1};

One way around the single-series limitation is to append separate time series to each other. If the series are long, the fact that prediction is impossible (between the end of one series and the beginning of the next) should not harm learning any more than that from noise. The network will learn the predictable parts and simply settle for high errors where the series are joined, satisfying the initial goal.

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