similar to: Cross correlations on many imported files.

Dear all, I have two series of returns and I want to find the cross-correlations between these two series. I know of the ccf, but it does not work as I'd like if i type ccf(x,y,lag.max=20,type="correlation",plot=FALSE) i got the error message Error in na.fail.default(ts.intersect(as.ts(x), as.ts(y))) : missing values in object So i found that somebody suggested to type

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

Hi all, I have a problem concerning my understanding of the cross-correlation (ccf) function in R. assume a time serie as: > t<-seq(0,6.28,by=0.01); > my_serie<-ts(sin(t),start=0,end=6.28,deltat=0.01) then I generate an other one shifted by 12 time points: > my_shifted_serie<-ts(sin(t),start=0+0.12,end=6.28+0.12,deltat=0.01) if I do the cross-correlation I get that the two

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

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

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

Hello All, I'm having trouble with the ccf() function. I am trying to do cross-correlation between two time-series, but I keep getting an error message I don't know what to do with. This what I type and the error message I get: > ccf(ts(mod[,1]),ts(mod[,2]),na.action='na.exclude',type='cor') Error in "colnames<-"(`*tmp*`, value = c("ts(mod[,

Hello everybody, I am sorry if my questions are too simple or not easily understandable. I’m not a native English speaker and this is my first analysis using this function. I have a problem with a cross correlation function and I would like to understand how I have to perform it in R. I have yearly data of an independent variable (x) from 1982 to 2010, and I also have yearly data of a variable

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

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

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 =

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)]) )

Hi all, I would like to know whether there is any function in R were i can find the cross-correlation of two or more multivariate (time series) data. I tried the function ccf() but it seems like to have two univariate datasets. Please let me know. sincerely, sandeep -- Sandeep Joseph PhD Post Doctoral Associate Center for Tropical & Emerging Global Diseases Paul D. Coverdell Center,

Fritz just put the first version of a new time series package to the contrib section at CRAN. The package is called "tseries.tgz" and provides a library for time series analysis. It contains acf Autocorrelation Function adf.test Augmented Dickey-Fuller Test amif Auto Mutual Information Function bds.test BDS Test

Fritz just put the first version of a new time series package to the contrib section at CRAN. The package is called "tseries.tgz" and provides a library for time series analysis. It contains acf Autocorrelation Function adf.test Augmented Dickey-Fuller Test amif Auto Mutual Information Function bds.test BDS Test

Dear R users, I am trying to use tseries' garch function in order to determine the volatility of a return series generated by quantmod. Here is the code that I am using: > library(quantmod) > getSymbols("AAPL") convert daily closing prices into continuous log returns > dret<-dailyReturn(AAPL,type='log') check to see that the autocorrelations decay >

I am doing an analysis that requires me to calculate correlations for a matrix of 15,000 rows x 50 columns. For each row I want to calculate the correlation to all other rows and then for each row, find the n (say 10) most correlated rows. If read in the 15,000 x 50 data from file and pass it to 'cor', this function quite appropriately (and very quickly) calculates all possible row by

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

Hi all, I have two time series that I would like to correlate but as they are autocorrelated, I am "pre-whitening" them first by fitting ARIMA models, then correlating their residuals....as described in https://onlinecourses.science.psu.edu/stat510/?q=node/75 However, http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm discusses some issues with ARIMA in R. In particular, for issue 2, if

There is now a preliminary version of a time series package in the R-devel snapshots, and we would welcome feedback on it. It is based in part on the packages bats (Martyn Plummer) and tseries (Adrian Trapletti) and in part on code I had or have written. (Thanks for the contributions, Martyn and Adrian!) Some of the existing ts code has been changed, for example to plot multiple time series, so