CODEWITHSUNDEEP
arraysmatlabmeanreshapemoving-average
Srishti M
Srishti M

Reputation: 543

Compute mean value over a sliding window in MATLAB

I have a time series data or considering a real valued data of length N. I want to create sub-blocks of length k, which is the window length. The value of k can be arbitrarily chosen. This creates problem since the window size is the same across the data. I want to store each subblock in an array. But I am stuck in creating sub-blocks of the data and to include a check so that the (mod(N, nseg)) nseg must be divisible by the data length.

N = 512; %length of the time series
data = rand(N,1);
window_length = 30;  %k
Nseg = floor(N/window_length) %Number of segments or blocks

Modified_Data = [mean(reshape(data,window_length,Nseg))]; %Throws error

Upvotes: 0

Views: 900

Answers (3)

Suever
Suever

Reputation: 65430

If you have the Image Processing toolbox you could use im2col to slide a specific block size over the entire time series. Each column of the output represents the data from one of those blocks.

values = im2col(data, [window_length 1], 'distinct');

Since it looks like you just want the mean over each block, you could also use blockproc to do this.

means = blockproc(data, [window_length, 1], @(x)mean(x.data));

If you do not have the Image Processing Toolbox, you can instead use accumarray to perform this task.

means = accumarray(floor((0:(N-1)).'/window_length) + 1, data, [], @mean);

If you want to discard any data that extends beyond a number which is divisible by window_length, you can do this with something like the following:

data = data(1:(numel(data) - mod(numel(data), window_length)));

If you want overlapping data, you'll either want to use straight-up convolution (the preferred method)

means = conv(data(:), ones(5, 1)/5, 'same');

Or you can create overlapping blocks with im2col by omitting the last input.

values = im2col(data, [window_length 1]);
means = mean(values 1);

Upvotes: 2

horchler
horchler

Reputation: 18484

If you have R2016a+, consider using the built-in movmean function:

N = 512; %length of the time series
data = rand(N,1);
window_length = 30;  %k
Modified_Data = movmean(data, window_length);

See the documentation for further details and other options.

Upvotes: 2

Uri Merhav
Uri Merhav

Reputation: 300

If I understand your question correctly, it's pretty straightforward:

filter(ones(N,1)/N,1,signal)

If you think about it filtering with [1/N 1/N 1/N...1/N] is exactly calculating the localized mean...

Upvotes: 0

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