arnaudsj
epsilon-SVR
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); }`
