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. sampl
Disclaimer: I worked this out on paper, but haven't yet had the opportunity to check it "in silico"...
You may be able to avoid loops or using cell arrays by doing some tricky cumulative sums, indexing, and calculating the means and standard deviations yourself. Here's some code that I believe will work, although I am unsure how it stacks up speed-wise to the other solutions:
[t,sortIndex] = sort(t); %# Sort the time points
x = x(sortIndex); %# Sort the data values
interval = 60; %# Interval size, in seconds
intervalIndex = floor((t-t(1))./interval)+1; %# Collect t into intervals
nIntervals = max(intervalIndex); %# The number of intervals
mu = zeros(nIntervals,1); %# Preallocate mu
sd = zeros(nIntervals,1); %# Preallocate sd
sumIndex = [find(diff(intervalIndex)) ...
numel(intervalIndex)]; %# Find indices of the interval ends
n = diff([0 sumIndex]); %# Number of samples per interval
xSum = cumsum(x); %# Cumulative sum of x
xSum = diff([0 xSum(sumIndex)]); %# Sum per interval
xxSum = cumsum(x.^2); %# Cumulative sum of x^2
xxSum = diff([0 xxSum(sumIndex)]); %# Squared sum per interval
intervalIndex = intervalIndex(sumIndex); %# Find index into mu and sd
mu(intervalIndex) = xSum./n; %# Compute mean
sd(intervalIndex) = sqrt((xxSum-xSum.*xSum./n)./(n-1)); %# Compute std dev
The above computes the standard deviation using the simplification of the formula found on this Wikipedia page.