Bad results obtained for LS-SVM regression. Any help?

Hello. Although I read many times the tutorial for this LS-SVM toolbox http://www.esat.kuleuven.be/sista/lssvmlab/ , I'm still having very bad results when I use it for regression.

Here is the code I execute:

% Initialisation of the model
model = initlssvm(X,Y,'f',[],[],'RBF_kernel','o');
% Tuning of the model, optimizing the choice of gam and sig2
model = tunelssvm(model,'simplex','crossvalidatelssvm',{10,'mae'});
% Training 
model = trainlssvm(model);
% Prediction of results
Yt=simlssvm(model,Xt)

The results obtained are far different from the initial ones. I really don't understand what I do wrong. Any one help me, please ? Thank you!

[Gustavo]

When you use the 'o' in initlssvm you are not normalizing the data, which is quite important in many cases for regression. Can you try without it?

[SANA]

My results are worse, but you can try giving different values of sig2 and gam in line: model = initlssvm(X,Y,'f',[gam],[sig2],'RBF_kernel','o'); This may solve the problem. I am getting a warning: *****> In crossvalidatelssvm (line 96) In tunelssvm>simanncostfun2 (line 485) In tunelssvm>@(x)simanncostfun2(x,model,costfun,costargs) (line 168) In csa (line 79) In tunelssvm (line 168) In tunelssvm (line 132) Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND = 5.551115e-17. Any idea how to fix this?