No idea about xcor function, but you can write a lower level code that does the job:
Autocorrelation is just the pearson correlation coefficient between a time series and the same time series but with a lag. In this code I do the lag-1 autocorrelation inside a moving window of 100 datapoints.
clc,clear,close all
T=2;A=2; %period and amplitude of signal
t=linspace(-4*T,4*T,1000);
x=mod(t,T)
x=A*(-x+A/2)%Periodic signal which it's autcorr. will be determined
%I transpose x so that it works with the code I had
x=x'
HalfMW=50;%half of the moving window
cont=0;
AutCorVsTime=zeros(cont,1);
for d=HalfMW+1:1000-HalfMW
cont=cont+1;
Chunk = x(d-HalfMW:d+HalfMW,1);
SizeChunk = size(Chunk,1);
AutCorVsTime(d,1)=corr(Chunk(2:SizeChunk,1),Chunk(1:SizeChunk-1,1),'rows','complete'); %Pearson correlation coefficient ignoring NaN
end
plot(AutCorVsTime)
And as you can see the resulting figure is indeed periodic.
If you change the size of the moving window the plot obviously changes.
