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sliding window algorithm for time-series data

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I have sample data and sampling frequency . Sample data points are 27900 and sampling frequency is 600 hz . I want to apply slidding window concept for my data. I want divide all data into set of 5 sec each with overlap of 4 sec. (i.e. 0-5, 1-6, 2-7, etc.). Please help me to apply slidding window concept.

Accepted Answer

Mathieu NOE
Mathieu NOE on 20 May 2021
HELLO
see demo below - second section implement a overlap method
all the best
clc
clearvars
% dummy data
data = rand(320,3); % data must be column oriented (number of rows = number of samples)
buffer = 25; % nb of samples for averaging
%% zero overlap averaging (unweighted)
[m,n] = size(data);
for ci=1:fix(m/buffer)
start_index = 1+(ci-1)*buffer;
stop_index = min(start_index+ buffer-1,m);
time_index(ci) = round((start_index+stop_index)/2); % time index expressed as sample unit (dt = 1 in this simulation)
avg_data(ci,:) =mean(data(start_index:stop_index,:)); %
end
figure(1),
plot(time_index,avg_data,'+-');
% return
%% averaging with overlap
clearvars
% dummy data
data = rand(320,3);
buffer = 25; % nb of samples in one buffer (buffer size)
overlap = 10; % overlap expressed in samples
%%%% main loop %%%%
[m,n] = size(data);
shift = buffer-overlap; % nb of samples between 2 contiguous buffers
for ci=1:fix((m-buffer)/shift +1)
start_index = 1+(ci-1)*shift;
stop_index = min(start_index+ buffer-1,m);
time_index(ci) = round((start_index+stop_index)/2); % time index expressed as sample unit (dt = 1 in this simulation)
avg_data(ci,:) = mean(data(start_index:stop_index,:)); %
end
figure(2),
plot(time_index,avg_data,'+-');
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