Why do parameters in output of my support vector regression are going to zero?

I am trying to predict next value in a time series from epsilon-support vector regression using libsvm library in matlab.Following is my code. From an excel file, I am taking first 3500 samples for training. Based on minimum cross validation mean square error, I want to decide how many lags to consider in order to predict the next value.

filename='test.xlsx';

F=xlsread(filename,'B2:B5001');

f_trainx =(1:3500)'; % time instants

f_trainy=F(1:3500); % observed values

f_testx=(3501:5000)';

f_testy=F(3501:5000);

for d=0:20 %% d= no. of lags considered for prediction

[C,gamma,eps] = meshgrid(-10:2:15, -20:2:3 , -20:2:3);

svm_ip=f_trainx(end:-1:end-d);

svm_op=f_trainy(end:-1:end-d);

s=3; % for epsilon svr

t=2; % for rbf kernel

folds = 5;

h=0;

for j=1:numel(C)

mse(j) = svmtrain(svm_op,svm_ip, ... sprintf('-s %d -t %d -c %f -g %f -p %f -v %d -h %d',s,t, 2^C(j), 2^gamma(j),2^eps(j), folds ,h)); end

[~,idx] = min(mse);

mse_cv =mse(idx);

best_C = 2^C(idx);

best_gamma = 2^gamma(idx) ;

best_eps = 2^eps(idx);

options = sprintf(' -s %d -t %d -c %f -g %f -p %f -%d',s,t,2^C(idx),2^gamma(idx),2^eps(idx),h);

model = svmtrain(svm_op, svm_ip, options)

x2 = 3501 ; % first value from test set

y2 = rand(1); % y2 is to be predicted,so taking any random value for it.

end

but on executing this code, for all iterations and for all values of d, I get the following output on console

optimization finished, #iter = 0

nu = 0.000000

obj = 0.000000, rho = -49.125125

nSV = 0, nBSV = 0

i.e the values of iter,nu,obj,nSV and nBSV are always 0. What is wrong with my code. Please help !!!