similar to: Comparing two time series?

I can't understand the results from cross-correlation function ccf() even though it should be simple. Here's my short example: ********* a<-rnorm(5);b<-rnorm(5) a;b [1] 1.4429135 0.8470067 1.2263730 -1.8159190 -0.6997260 [1] -0.4227674 0.8602645 -0.6810602 -1.4858726 -0.7008563 cc<-ccf(a,b,lag.max=4,type="correlation") cc Autocorrelations of series 'X',

Dear R-help, How could a cross-correlation plot be optimized such that the relationship between seasonal time-series can be studied? We are working with strong seasonal time-series and derived a cross-correlation plot to study the relationship between time-series. The seasonal variation however strongly influences the cross-correlation plot and the plot seems to be ?rather? symmetrical (max

Dear All, I have 2 time-series data sets and would like to check the cross correlation. These data sets were set as a zoo object, called data, and in general look like: V1 V2 2007-01-01 00:00:00 0.0 0.176083 2007-01-01 01:00:00 0.0 0.176417 2007-01-01 02:00:00 0.0 0.175833 2007-01-01 03:00:00 0.0 0.175833 2007-01-01

Hello, I have a dataframe containing various time series (not time series objects though!)with hourly time steps. I?d like to perform ccf for I need to know the correlation factors for different lags. Here is an example: x<-as.POSIXct(c("2008-12-25 16:00:00", "2008-12-25 17:00:00", "2008-12-25 18:00:00", "2008-12-25 19:00:00", "2008-12-25

I have 4 univariate time series that I believe have correlation between them, I want to create a VAR model between them all. However I have an issue as 3 of them are the same length, however the 4th is smaller. meaning that i cannot use the 4th variable , is there anyway R can get round this issue? > length(tstemp) [1] 746 > length(tspres) [1] 746 > length(tswind) [1] 746 >

Hi list, I'm working on time series of (bio)physical data explaining (or not) the net ecosystem exchange of a system (+_ CO2 in versus CO2 out balance). I decomposed the time series of the various explaining variable according to scale (wavelet decomposition). With the coefficients I got from the wavelet decomposition I applied a (multiple) regression, giving some expected results. The net

Dear all, I am trying to avoid a for loop here and wonder if the following is possible: I have a data.frame with 6 columns and i want to get a cross-correlogram (by using ccf) . Obivously ccf only accepts two columns at once and then returms a list. In fact, with a for loop i?d do the following for (i in 1:6) { x[[i]]=ccf(mydf[,i],mydf[,6]) } Is there any chance to the same with

hello, i have been using ccf() to look at the correlation between lightning and electrogamnetic data. for the most part it has worked exactly as expected. however, i have come across something that puzzles me a bit: > x <- c(1, 0, 1, 0, 1, 0) > y <- c(0, 0, 0, 0, 0, 0) > ccf(x, x, plot = FALSE) Autocorrelations of series 'X', by lag -4 -3 -2 -1 0

Hi all, I have two time series data (say x and y). I am interested to calculate the correlation between them and its confidence interval (or to test no correlation). Function cor.test(x,y) does the test of no correlation. But this test probably is wrong because of autocorrelated data. ccf() calculates the correlation between two series data. But it does not

Hi. It's my understanding that a cross-correlation function of vectors x and y at lag zero is equivalent to their correlation (or covariance, depending on how the ccf is defined). If this is true, could somebody please explain why I get an inconsistent result between cov() and ccf(type = "covariance"), but a consistent result between cor() and ccf(type = "correlation")? Or

I am new to R and new to time series modeling. I have a set of variables (var1, var2, var3, var4, var5) for which I have historical yearly data. I am trying to use this data to produce a prediction of var1, 3 years into the future. I have a few basic questions: 1) I am able to read in my data, and convert it to a time series format using 'ts.' data_ts <- ts(data, start = 1988, end =

Dear R-users, I have been using R and its core-packages with great satisfaction now for many years, and have recently started using the "ccf" function (part of the "stats" package version 2.16.0), about which I have a question. The "ccf"-algorithm for calculating the cross-correlation between two time series always calculates the mean and standard deviation per time

Dear All, I am studying some process measurement time series in R and trying to identify time delays using cross-correlation function ccf. The results have however been bit confusing. I found a couple of years old message about this issue but unfortunately wasn't able to find it again for a reference. For example, an obvious time shift is observed between the measurements y1 and y2 when the

Hi all, I am getting different results from ccf and cor, Here is a simple example: set.seed(100) N <- 100 x1 <- sample(N) x2 <- x1 + rnorm(N,0,5) ccf(x1,x2)$acf[ccf(x1,x2)$lag == -1] cor(x1[-N], x2[-1]) Results: > ccf(x1,x2)$acf[ccf(x1,x2)$lag == -1] [1] -0.128027 > cor(x1[-N], x2[-1]) [1] -0.1301427 Thanks, Tal ----------------Contact

Hi, I have missing values in my time series. "na.action = na.pass" works for acf and pacf. Why do I get the following error for the ccf? > ts(matrix(c(dev$u[1:10],dev$q[1:10]),ncol=2),start=1,freq=1) Time Series: Start = 1 End = 10 Frequency = 1 Series 1 Series 2 1 68.00000 138.4615 2 70.00000 355.5556 3 68.76000 304.3200 4 68.00000 231.4286 5 69.74194 357.4963 6

Hello! I found the ccf function gives different estimates than the simple lag correlations. Why is that? This is my code: set.seed(20) x<-rnorm(20) y<-x+rnorm(20,sd=0.3) print("R CCF:") print(ccf(x,y,lag.max=2,plot=F)) myccf<- c( cor(y[-(1:2)],x[-(19:20)]) , cor(y[-1],x[-20]), cor(y,x), cor(x[-1],y[-20]),cor(x[-(1:2)],y[-(19:20)]) )

I browsed the web and the archive, and apart from someone asking whether anyone else had rsync problems after installing OpenSSH 3.4, I came up empty. Can anyone point me in the right direction to debug this? I've got over 50 GB to keep in sync, and don't know of another elegant way to do it. Environment: source system = bb: - Linux version 2.4.7-10smp

I am trying to run a cross-correlation using the "ccf()" function. When I select plot = TRUE in the ccf() I get a graph which has ACF on the y-axis, which would suggest that these y-values are the auto-correlation values. How should I adjust the code to produce a plot that provides the cross-correlation values? Here is my code: w002dat <-

Hi, I have been running the ccf() function to find cross-correlations of time series across various lags. When I give the option of plot=TRUE, I get a plot that gives me 95% confidence interval cut-offs (based on sample covariances) for my cross-correlations at each lag. This gives me a sense of whether my cross-correlations are statistically significant or not. However, I am unable to get R to

Does anyone know of any R code for computing partial cross-correlation? I have examples of cross correlation functions (ccfs) that are not smooth but rather consist of a peak of several high values in consecutive lags, with sharp drops on either side. This indicates that y(t) is a function of some average of x(t-tau) at the set of lags tau over which the ccf is high. I could sort out these