First, generate two random time series, then calculate the mean, standard deviation, median, maximum, minimum, and correlation coefficient matrix of these sequences, and finally display the processing results.
% Clear all; clc; close all; % Generate random data A1=3.0*randn(1,2000); B1=5.0*randn(1,2000); % Mean disp(‘Mean values’); mean_A=mean(A1); mean_B=mean(B1); % Standard deviation disp(‘Standard deviations’); std_A=std(A1); std_B=std(B1); % Maximum value disp(‘Maximum values’); max_A=max(A1); max_B=max(B1); % Minimum value disp(‘Minimum values’); min_A=min(A1); min_B=min(B1); % Median disp(‘Median values’); median_A=median(A1); median_B=median(B1); % Sort from smallest to largest disp(‘Sorting’); order_A=sort(A1); order_B=sort(B1); figure(3); plot(order_A,’b-‘); title(‘Sorted A1’); xlabel(‘A1’); figure(4); plot(order_B,’b-‘); title(‘Sorted B1’); xlabel(‘B1’); % Covariance disp(‘Covariance matrix’); covAB=cov(A1,B1); % Correlation coefficient disp(‘Correlation coefficient matrix’); corAB=corrcoef(A1,B1); %%%%%%%%%%%%%%%%%%%%%% figure(1); plot(A1,’b-‘); title(‘A1’); xlabel(‘A1’); ylabel(‘x’); legend(‘A1’); grid on; grid on; figure(2); plot(B1,’b-‘); title(‘B1’); xlabel(‘B1’); ylabel(‘x’); legend(‘B1’); grid on; grid on;