带有 LIBSVM 的 epsilon-SVR

Posted 2023-03-12

【中文标题】带有 LIBSVM 的 epsilon-SVR

【英文标题】：epsilon-SVR with LIBSVM

【发布时间】：2018-09-15 20:49:47

【问题描述】：

我正在尝试使用 Java 中 LIBSVM 的 epsilon-SVR 预测单变量时间序列（我的数据由两列组成，一个时间戳和一个数值）。

当我不使用特性时，只考虑数组索引作为特性（我知道它不可信）它总是返回相同的值。如果我使用滑动窗口，即预测时间 t 的值的特征是时间 t-1、t-2、...、t-sliding_window 的值，它总是返回 NaN。

我按照上面的方法训练模型：

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;

得到的预测如下：

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);

我做错了什么？ 提前致谢。

【参考方案1】：

显然，对数据进行归一化并将 gamma 参数的值更改为 1 解决了这个问题。

当数据域太大时，在构建支持向量回归模型时对其进行归一化是一种很好的方法，可以提高预测质量和执行时间。