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I have a bunch of times-series each described by two components, a timestamp vector (in seconds), and a vector of values measured. The time vector is non-uniform (i.e. sampled at non-regular intervals) I am trying to compute the mean/SD of each 1-minutes interval of values (take X minute interval, compute its mean, take the next interval, ...). My current implementation uses loops. This is a sample of what I have so far: t = (100:999)' + rand(900,1); %' non-uniform time x = 5*rand(900,1) + 10; % x(i) is the value at time t(i) interval = 1; % 1-min interval tt = ( floor(t(1)):interval*60:ceil(t(end)) )'; %' stopping points of each interval N = length(tt)-1; mu = zeros(N,1); sd = zeros(N,1); for i=1:N indices = ( tt(i) <= t & t < tt(i+1) ); % find t between tt(i) and tt(i+1) mu(i) = mean( x(indices) ); sd(i) = std( x(indices) ); end I am wondering if there a faster vectorized solution. This is important because I have a large number of time-series to process each much longer than the sample shown above.. Any help is welcome. Thank you all for the feedback. I corrected the way t is generated to be always monotonically increasing (sorted), this was not really an issue.. Also, I may not have stated this clearly but my intention was to have a solution for any interval length in minutes (1-min was just an example)

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