search for: autocorelation

Hi all, I am trying to find the autocorrelation of some time series. I have say 100 files, some files have only missing values(-99.99, say). I dont want to exclude these files as they represent some points in a grid. But when the acf command is issued i get an error. Error in plot.window(...) : need finite 'ylim' values In addition: Warning messages: 1: In min(x) : no

Should "dwtest" and "durbin.watson" be giving me the same DW statistic and p-value for these two fits? library(lmtest) library(car) X <- c(4.8509E-1,8.2667E-2,6.4010E-2,5.1188E-2,3.4492E-2,2.1660E-2, 3.2242E-3,1.8285E-3) Y <- c(2720,1150,1010,790,482,358,78,35) W <- 1/Y^2 fit <- lm(Y ~ X - 1) dwtest(fit,alternative="two.sided")

...al. For this purpose I used the auto.arima function in forecast package. After fitting residuals at each grid in space, the auto correlations are still significant ( but < 0.2). This make me think that the data could be spatially correlated as well. In such case is it necesary to remove spatial autocorelations before fitting models in time and are there some methods available in R to remove the spatial autocorrelations. Thanks nuncio -- Nuncio.M Research Scientist National Center for Antarctic and Ocean research Head land Sada Vasco da Gamma Goa-403804 [[alternative HTML version deleted]]

...st row Open, High, Low, Close, Volume wig20 <- read.csv("wig20.txt", sep=";", dec=",") #multiply by 100, because sometimes it's easier to converge the model r <- 100*diff(log(wig20$CLOSE)) kpss.test(r) pp.test(r) acf(r) pacf(r) #if there is no significant autocorelation: y <- r - mean(r) fit <- garch(y, order = c(1,1)) summary(fit) plot(fit) #If you need some particular results for further testing, then use: ch <- predict(fit, genuine=TRUE) e <- fit$residuals #end do what you want or draw any other result this way That's just the basic, but then...

This idea is so simple that I'm sure it must have been thought of before, and discarded, since AFAIK it's not used anywhere. I did a quick web search but that didn't turn up much, so I figured I'd put it up for discussion here anyway. How about using large-scale repetition in audio compression? I'm thinking of redundancy in repeated pieces of a song, ie a chorus.

Respected Sir I tried the strucchange My data is attached. However I tried the attached commands (last save.txt) to perform Bai Perron 2003... I t worked well but in the end it is giving warning that overlapping confidence interval... I am not sure how to proceed... Please Help Me Thanking You Ayanendu Sanyal -- Please have a look at our new mission and contribute into it (cut and paste the