time-lagged correlation coefficient between two time series

I am trying to find the time-lagged correlation coefficient between two time series (two sea pressure time series at different points). I have two series of exactly the same length and with the same number of records, and I just want to see at what time lag the two series have the highest correlation. I obtained the correlation coeficient from corr.m but are looking for one that handles lag options to obtain the highest correlation at a given time.

Any help would be appreciated.

Thanks.

[Kim]

I would also be very grateful for any input on this question. I have the same problem (but EEG data). I can see in the visual representation that there may be a time lag in the data of about 50 ms. I have tried xcorr with different lags, but am having trouble, 1. figuring out the significance of the return, 2. exactly how to interpret the stem plot. It should probably be obvious but I am uncertain that I have it right. Thanks!

[Youssef Khmou]

hi, did you only use "corr.m"? or you also used "xcorr.m"?

[wasswa peter]

Hello,I think better use R,here is the code I developed to find the time lag between meteorological drought and hydrological drought

library("ggpubr")

library("Kendall")

library("trend")

library("astsa")

my_data <- read.csv(file.choose())

my_data

names(my_data)

attach(my_data)

x <-SPI3

y <-SDI3

SPI3=ts (SPI3)

SDI3 = ts(SDI3)

ccfvalues = ccf(SPI3, SDI3)

ccfvalues

ccf (SPI3, SDI3, main = "SPI3 & SDI3 FOR STATION B",xlab = "Lag Time (months)",ylab= "Autocorrelation")

Answer 1

[Naomi Krauzig]

In case this is still an open question [r,lags] = xcorr(x,y) also returns the lags at which the correlations are computed.

Answer 2

[Youssef Khmou]

hi,i propose two ways :

1)as you have to series P1, P2 of length N both:

 y=xcorr(P1,P2); % Len= 2*N-1
 plot(y)

2)Or logically, you can compute the quadratic error, as

( P1(i,j)-P2(i,j) )²  for 1<=i,j <=N :

 err=(P1-P2).^2;
 f=1./err;
 figure, plot(f), xlabel(' Measurments'), ylabel(' E^2'),
 [Max,time_index]=max(f);

The "time_index" is when there is high correlation between p1 and p2 .