平滑曲线几种算法
1 .二次指数平滑法求预测值
/** * 二次指数平滑法求预测值 * @param list 基础数据集合 * @param year 未来第几期 * @param modulus 平滑系数 * @return 预测值 */ private static Double getExpect(Listlist, int year, Double modulus ) { if (list.size() < 10 || modulus <= 0 || modulus >= 1) { return null; } Double modulusLeft = 1 - modulus; Double lastIndex = list.get(0); Double lastSecIndex = list.get(0); for (Double data :list) { lastIndex = modulus * data + modulusLeft * lastIndex; lastSecIndex = modulus * lastIndex + modulusLeft * lastSecIndex; } Double a = 2 * lastIndex - lastSecIndex; Double b = (modulus / modulusLeft) * (lastIndex - lastSecIndex); return a + b * year; }
2.最小二乘法曲线拟合
/** * 最小二乘法曲线拟合 * @param data * @return */ public static Listpolynomial(List data,int degree){ final WeightedObservedPoints obs = new WeightedObservedPoints(); for (int i = 0; i < data.size(); i++) { obs.add(i, data.get(i)); } /** * 实例化一个2次多项式拟合器 */ final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(degree);//degree 阶数,一般为 3 /** * 实例化检索拟合参数(多项式函数的系数) */ final double[] coeff = fitter.fit(obs.toList());//size 0-3 阶数 List result = new ArrayList<>(); for (int i = 0; i < data.size(); i++) { double tmp=0.0; /** * 多项式函数f(x) = a0 * x + a1 * pow(x, 2) + .. + an * pow(x, n). */ for (int j = 0; j<= degree; j++) { tmp+= coeff[j]* Math.pow(i,j); } result.add(tmp); } return result; }
3.5点3次平滑曲线
public static Double[] Mean5_3(Double[] a, int m) { Double[] b = new Double[a.length]; int n = a.length; for (int k = 0; k < m; k++) { b[0] = (69 * a[0] + 4 * (a[1] + a[3]) - 6 * a[2] - a[4]) / 70; b[1] = (2 * (a[0] + a[4]) + 27 * a[1] + 12 * a[2] - 8 * a[3]) / 35; for (int j = 2; j < n - 2; j++) { b[j] = (-3 * (a[j - 2] + a[j + 2]) + 12 * (a[j - 1] + a[j + 1]) + 17 * a[j]) / 35; } b[n - 2] = (2 * (a[n - 1] + a[n - 5]) + 27 * a[n - 2] + 12 * a[n - 3] - 8 * a[n - 4]) / 35; b[n - 1] = (69 * a[n - 1] + 4 * (a[n - 2] + a[n - 4]) - 6 * a[n - 3] - a[n - 5]) / 70; } return b; }