Arduino Cross Correlation?
Given the time scale of the measurements, performance is not an issue.
And since you are not computing in real-time, as the samples are being
measured, you can apply the definition of cross-correlation as is, and
compute it in a double loop. The only difficulty is figuring out that
cross-correlation may not be the proper tool for the job... and finding
the right one! My assumption is that you want to estimate a rotation
angle between the two measurements.

The mathematical definition of cross-correlation assumes infinite
arrays. In practice, cross-correlation is often used to locate a short
pattern inside a long signal. In this case, the computation is done only
for shifts where the pattern completely overlaps the signal. Then the
length of the result is

length(result) = length(signal) − length(pattern) + 1


In your case, since your pattern is a previously measured signal, this
would give a single-sample result, which is useless.

You could instead compute the integral for all shifts where there is
any overlap between the pattern and the signal. This is equivalent to
zero-padding the signal. This has the drawback of biasing the result: if
your signal and pattern are both featureless (positive constants), you
will find a very nice peak at zero shift!

You can remove this bias by subtracting the average from both the signal
and the pattern, and correlating (signal −&nbsp;avg(signal)) with
(pattern −&nbsp;avg(pattern)). This still carries a bias for “big”
features. Imagine for example that, after removing the averages, you
have

pattern = [  0,   0, −20, +20,   0,   0];
signal  = [−50, +50,   0,   0, −20, +20];


You would expect your correlator to find a good match between the
[−20, +20] of the pattern and the [−20, +20] of the signal. The
correlation is, however, much better with the [−50, +50] of the signal.
You may try to alleviate this by computing a normalized
cross-correlation instead, but you will at best make both matches
equally good.

My suggestion is that you completely forget about correlations and
instead think in terms of “goodness of fit”. Correlations are good for
finding scaled and shifted copies of a pattern inside a signal. You are
looking for shifted only copies, with no scaling. Your question should
be: if you shift the pattern by some amount, how well does it fit the
signal? What amount of shift gives the better fit? The canonical answer
to all “goodness of fit” questions is to minimize the RMS difference
between the two: you compute the sum

S = Σ(signal − shifted(pattern))²


Then the RMS difference is

RMS(difference) = √(S/N)


where N is the number of samples involved in the sum. The most likely
shift is the one that minimizes this error. In practice, you do not need
the square root, you just minimize

RMS(difference)² = Σ(signal − shifted(pattern))² / N


You can implement this formula as-is, it's completely straightforward.
Just beware that N depends on the shift, as it's the length of the
overlap. And avoid going to very small overlaps (i.e. large shifts).

Now, if you expand the square inside the sum, you may find that, after
all, this is not so far from a cross-correlation... And BTW, all this
has nothing to do with Arduino.