epsilon-SVR

[joao-prg]

I am trying to forecast univariate time series (my data is composed by two columns, a timestamp and a numeric value) using epsilon-SVR from LIBSVM in Java.

When I do not use features, considering only the array index as feature (which I know it is not trustworthy) it returns always the same value. If I use a sliding window, i.e, the features to predict the value at time t are the values at time t-1,t-2,..., t-sliding_window, it always returns NaN.

I do as above to train the model:

`public svm_model train(double[] series, int svmType, int kernelType, int degree, double gamma, double coef0, double C, double eps, double p, int shrinking, int nFeatures)
{
series = normalize(series)
svm_parameter params = new svm_parameter();
svm_problem problem = new svm_problem();
svm_node node = null;
//----------Set parameters----------
params.svm_type = svmType;
params.kernel_type = kernelType;
params.degree = degree;
params.gamma = 1/nFeatures;
params.coef0 = coef0;
params.C = C;
params.eps = eps;
params.cache_size=100;
params.p = p;
params.shrinking= shrinking;
//----------Define problem----------
problem.l = series.length;
problem.y = series;
problem.x = new svm_node[series.length][];
for(int i=0;i<series.length;i++)
{
problem.x[i] = new svm_node[1];
node = new svm_node();
node.index = 0;
node.value = i;
problem.x[i][0] = node;
}
//----------Generate model----------
svm_model svm_model = svm.svm_train(problem,params);
return svm_model;
}

public svm_model trainSlidingWindow(double[] series, int svmType, int kernelType, int degree, double gamma, double coef0, double C, double eps, double p, int shrinking, int nFeatures, int slidingWindow)
{
series = normalize(series)
svm_parameter params = new svm_parameter();
svm_problem problem = new svm_problem();
svm_node node = null;
//----------Set parameters----------
params.svm_type = svmType;
params.kernel_type = kernelType;
params.degree = degree;
params.gamma = 1/nFeatures;
params.coef0 = coef0;
params.C = c;
params.eps = eps;
params.cache_size=100;
params.p=p;
params.shrinking= shrinking;
//----------Define problem----------
problem.l = series.length;
problem.y = series;
problem.x = new svm_node[series.length][slidingWindow];
for(int i=0;i<series.length;i++)
{
problem.x[i] = new svm_node[slidingWindow];
for(int j=0; j<slidingWindow;j++)
{
node = new svm_node();
node.index = slidingWindow-(j+1);
if(i-(j+1) <0)
node.value = Double.NaN;
else
node.value = series[i-(j+1)];
problem.x[i][j] = node;
}
}
//----------Generate model----------
svm_model svm_model = svm.svm_train(problem,params);
return svm_model;
}`

The forecasts are obtained as follows:

`public double[] predict(double[] series, svm_model model, int steps)
{
series = normalize(series);
double[] yPred = new double[steps];
for(int i=0;i<steps;i++)
{
svm_node[] nodes = new svm_node[1];
svm_node node = new svm_node();
node.index = 0;
node.value = series.length + i;
nodes[0] = node;
yPred[i] = svm.svm_predict(model,nodes);
}
return denormalize(yPred);
}

public double[] predictSlidingWindow(double[] series, svm_model model, int steps, int slidingWindow)
{
series = normalize(series);
double[] yPred = new double[steps];
double[] aux = new double[slidingWindow+steps];
System.arraycopy(series,series.length-slidingWindow,aux,0, slidingWindow);
for(int i=0;i<steps;i++)
{
svm_node[] nodes = new svm_node[slidingWindow];
for(int j=0;j<slidingWindow;j++)
{
svm_node node = new svm_node();
node.index = slidingWindow-(j+1);
node.value = aux[i+j];
nodes[j] = node;
}
yPred[i] = svm.svm_predict(model,nodes);
aux[slidingWindow+i] = yPred[i];
}
return denormalize(yPred);
}`