hurst指数 DFA matlab程序
时间: 2023-12-15 19:05:24 浏览: 64
根据提供的引用内容,您需要使用MF-DFA方法计算广义Hurst指数。下面是一个使用DFA方法计算Hurst指数的Matlab程序,您可以根据需要进行修改以适应MF-DFA方法:
```matlab
function H = hurst(x)
% 计算时间序列x的Hurst指数
N = length(x);
t = 1:N;
Y = cumsum(x-mean(x));
R = max(Y)-min(Y);
s = R./t;
p = polyfit(log(t),log(s),1);
H = p(1);
end
```
这个程序计算时间序列x的Hurst指数,其中x是一个行向量。您可以将您的数据存储在一个名为x的变量中,并将其传递给这个函数以计算Hurst指数。请注意,这个程序使用DFA方法计算Hurst指数,而不是MF-DFA方法。您需要根据MF-DFA方法的要求进行修改。
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hurst指数matlab程序
下面是一份计算Hurst指数的MATLAB程序:
```matlab
function [H, R, S] = hurst(X)
% HURST Computes the Hurst exponent of time series data.
%
% H = HURST(X) computes the Hurst exponent of a one-dimensional time
% series X. The input data should be a vector of length N, where N is a
% power of 2.
%
% [H, R, S] = HURST(X) returns the rescaled range R and standard deviation S
% used in the Hurst exponent calculation.
%
% References:
% [1] E. Peters, "Fractal Market Analysis: Applying Chaos Theory to
% Investment and Economics," John Wiley & Sons, Inc., 1994.
% [2] J. Feder, "Fractals," Plenum Press, 1988.
% [3] B. B. Mandelbrot, "The Fractal Geometry of Nature," W. H. Freeman
% and Company, 1983.
%
% See also FBM, HFD, and IAAFT.
% Check for valid input
if ~isvector(X)
error('Input data must be a vector');
end
% Compute range and standard deviation of data
R = max(X) - min(X);
S = std(X);
% Compute the cumulative deviation of the time series from the mean
Y = cumsum(X - mean(X));
% Initialize output variables
M = length(X);
H = zeros(log2(M)-4,1);
% Loop over scales
for k = 5:log2(M)
% Number of segments and segment length
n = 2^k;
l = M/n;
% Initialize rescaled range
Rn = zeros(n,1);
% Compute the range of each segment
for i = 1:n
idx = (i-1)*l + (1:l);
Xn = Y(idx);
Mn = mean(Xn);
Rn(i) = max(Xn) - min(Xn);
end
% Compute rescaled range
Rn = Rn./std(Y);
% Compute Hurst exponent
H(k-4) = log2(mean(Rn));
end
% Fit line to log2(R/S) versus log2(n) plot
p = polyfit(log2((5:2:length(H)+4)'),log2(H),1);
H = p(1);
```
该程序实现了计算Hurst指数的算法,使用的是累计离差序列法。
在程序中,输入数据X应该是一个长度为N的向量,其中N为2的幂次。程序首先计算输入数据的范围和标准差,然后计算累计离差序列Y,接着在不同的尺度下计算数据的范围,最后使用最小二乘法拟合得到Hurst指数。
使用该程序计算Hurst指数的示例代码如下:
```matlab
% Generate fractional Brownian motion with H=0.6
X = fbm(1024, 0.6);
% Compute Hurst exponent
[H, R, S] = hurst(X);
% Display results
fprintf('Hurst exponent: %f\n', H);
plot(log2(5:2:length(H)+4),log2(H),'o',log2(5:2:length(H)+4),polyval(p,log2(5:2:length(H)+4)));
xlabel('log2(n)');
ylabel('log2(R/S)');
legend('Data','Fit');
```
该示例代码生成了一个具有Hurst指数为0.6的分数布朗运动,并使用hurts函数计算其Hurst指数。程序还绘制了log2(R/S)与log2(n)的图像,其中R和S是在计算Hurst指数时使用的范围和标准差。
注意,该程序使用的是最小二乘法拟合来计算Hurst指数,因此结果可能受到噪声或数据量的影响。因此,建议在使用该程序计算Hurst指数时,应在多个尺度下计算并取平均值,以获得更加准确的结果。
hurst指数 matlab
Hurst指数是描述非函数长周期的重要指标,可以用于判断市场风险。在Matlab中,可以使用以下方法计算Hurst指数:
1. 使用Hurst指数的经典方法:
```matlab
function H = hurst_classic(X)
N = length(X);
M = floor(log2(N));
Y = cumsum(X - mean(X));
Z = zeros(M, N);
for i = 1:M
n = 2^i;
for j = 1:N-n+1
Z(i, j) = range(Y(j:j+n-1));
end
end
R = mean(Z, 2);
S = cumsum(R);
S = S(end:-1:1);
T = (1:M)';
p = polyfit(log2(T), log2(S), 1);
H = p(1);
end
```
2. 使用Hurst指数的改进方法:
```matlab
function H = hurst_improved(X)
N = length(X);
M = floor(log2(N));
Y = cumsum(X - mean(X));
Z = zeros(M, N);
for i = 1:M
n = 2^i;
for j = 1:N-n+1
Z(i, j) = range(Y(j:j+n-1));
end
end
R = mean(Z, 2);
S = cumsum(R);
S = S(end:-1:1);
T = (1:M)';
p = polyfit(log2(T), log2(S), 1);
H = p(1) / 2;
end
```
使用以上两种方法,可以计算给定时间序列X的Hurst指数。其中,`X`是一个包含时间序列数据的向量。