Hi,
I have an algorithm to forecast time series relying on observed values of the time series in the past. The algorithm is based on several so called experts and based on their performance in the past a convex combination of the experts is used for final prediction.
In the case of Neural Networks, one of the parameters of the experts is the amount of neurons h used. Simplified I have a loop
for i=1:n
net = newfit(inputs(:,1:i),targets(1:i),h);
net.divideParam.trainRatio=75/100;
net.divideParam.valRatio=25/100;
net.trainParam.showWindow=false;
net=train(net,inputs(:,1:i),targets(1:i));
estimate=sim(net,x_eval);
...
end
So in every iteration the network trained only difers a little bit, because only one pair of (inputs,targets) is added for training, the rest remains unchanged. Is there a way to use this fact and accelerate the whole process, like "retraining"?
Thanks
Daniel
