Function gen_rand
Generates random time series, distorted by white noise, which is based on randomly generated change points
Contents
Input
- nb_data - number of data points from which time series is built
- nb_ch_pts - number of expected change points that are to be generated
- nb_repeat - number of trials of random data generation
- snr - Signal to Noise Ratio in dB included in white noise
Output
- result - generated random time series with random change points
- change_points - generated randomly change points used as a reference
Copyrights
(C) All rights reserved.
The code may be used free of charge for non-commercial and educational purposes, the only requirement is that this text is preserved within the derivative work. For any other purpose you must contact the authors for permission. This code may not be redistributed without written permission from the authors.
ABOUT: This software implements our approach to detect changes in multi-variate time series
IMPORTANT: If you use this software you should cite the following in any resulting publication:
[1] Michal Staniszewski, Agnieszka Skorupa, Lukasz Boguszewicz, Maria Sokol and Andrzej Polanski. Quality Control Procedure Based on Partitioning of NMR Time Series.
function [result,change_points]=gen_rand(nb_data,nb_ch_pts,nb_repeat,snr) offset = 10; %minimal distance between two change points change_points = sort(randperm(nb_data-offset, nb_ch_pts)); %random change points change_points = change_points(change_points>offset); while size(change_points,2)<nb_ch_pts %ensuring given offset change_points = sort(randperm(nb_data-offset, nb_ch_pts)); %random change points change_points = change_points(change_points>offset); end change_points = [1 change_points nb_data]; result=zeros(nb_data-1,nb_repeat); segments = rand(nb_repeat,nb_ch_pts+1); %random values of mean %iterate over number of repeats for i=1:nb_repeat data = []; %iterate over number of time series for j=1:nb_ch_pts+1 probe = ones(1,change_points(j+1)-change_points(j))*segments(i,j); data = [data probe]; end data = awgn(data,snr,'measured')'; result(:,i) = data; end % plot(data) end
ans = 0.7873 0.7670 0.7528 0.7720 0.7484 0.7553 0.7475 0.7368 0.8054 0.7429 0.7518 0.7682 0.7363 0.7301 0.7764 0.7520 0.7245 0.7393 0.7463 0.7452 0.7499 0.7322 0.7488 0.7308 0.7382 0.7522 0.7736 0.7746 0.7227 0.7745 0.7262 0.7597 0.7560 0.7491 0.7376 0.7230 0.7283 0.7532 0.7448 0.7581 0.7397 0.7143 0.7102 0.7246 0.7574 0.7407 0.7548 0.7272 0.7372 0.2511 0.3936 0.4182 0.3870 0.3835 0.3921 0.3745 0.4085 0.4163 0.4119 0.3919 0.3669 0.4235 0.3997 0.4182 0.3994 0.3953 0.3672 0.4099 0.4406 0.3915 0.4072 0.4073 0.4317 0.3731 0.3794 0.4072 0.5306 0.5268 0.5392 0.5533 0.6135 0.2997 0.3451 0.2586 0.3124 0.2853 0.2999 0.3081 0.3115 0.3231 0.3174 0.3130 0.2926 0.3041 0.3151 0.2787 0.3220 0.3211 0.3164