similar to: Forecasting observations in ARFIMA

Hi, I am new to using R for Time series analysis. I was wondering if there are any functions that can convert ARMA or ARIMA time series into their corresponding AR or MA time series representations (by calculating the corresponding AR or MA coefficients). Thanks a lot Kris.

>>>>> akshay kulkarni >>>>> on Wed, 31 May 2023 20:55:33 +0000 writes: > dear members, > I am using arfima() from forecast package to model a time > series. The following is the code: >> LYGH[[202]] > [1] 45.40 3.25 6.50 2.15 >> arfima(LYGH[[202]]) > Error in .fdcov(x, fdf$d, h, nar = nar, nma = nma,

Dear Martin, Sad that the bug is beyond your ken... Fortunately, the error happens only rarely...The length of LYGH was 719 and there were only two such errors..I will just replace them with NA and make do. By the by, what if I send LYGH as an attachment to your actual mail ( not the r-help mail)? Will it help? Can you then pinpoint the cause? Or should I raise a bug

dear members, I am using arfima() from forecast package to model a time series. The following is the code: > LYGH[[202]] [1] 45.40 3.25 6.50 2.15 > arfima(LYGH[[202]]) Error in .fdcov(x, fdf$d, h, nar = nar, nma = nma, hess = hess, fdf.work = fdf$w) : NA/NaN/Inf in foreign function call (arg 5) I tried viewing .fdcov() with the following code:

Hi all, ok, can someone tell me which header fields that the "Sender" function in sieve looks at ??? I've created a sieve rule to move a mail to a directory that looks like this : # rule:[File Message] if anyof (address :contains "Sender" "Dude") { fileinto "TestFolder"; } And the emails headers look like this : Received: (qmail 5811 invoked

Hello. I'm trying to make k-step-ahead forecasts using ARFIMA(0, d, 0) models by taking the first T+k-1 coefficients in the binomial expansion of (1-B)^d, regarding (1-B)^d x(T+k) as an AR(T+k-1) on x(T+k), where x(T) is the series value at time T and k = 1, 2, 3, . That is, I forecast the series k values forward using the first T+k-1 coefficients in the binomial expansion of (1-B)^d as

Please I want to perform full data analysis using ARFIMA model but I dont know the right package that can perform all the necessary test on the time series data. ERIC AIDOO [[alternative HTML version deleted]]

Dear R users, I'm trying to use the "strucchange" package to determine structural breaks in an ARFIMA model. Unfortunately I'm not so familiar with this topic (and worse, I'm a beginner in R), so I don't know exactly how to specify my model so that the "Fstats","sctest" and "breakpoint" functions to recognize it and to calculate the

Hi All, I have a problem trying to forecast using the dlm package, can anyone offer any advise? I setup my problem as follows, (following the manual as much as possible) data for example to run code CostUSD <- c(27.24031,32.97051, 38.72474, 22.78394, 28.58938, 49.85973, 42.93949, 35.92468) library(dlm) buildFun <- function(x) { dlmModPoly(1, dV = exp(x[1]), dW = exp(x[2])) } fit <-

Hello all, This is a request for help from somebody who has the Ox interfaces working in R. I am trying to get the Ox interfaces working for Arfima and Garch modelling. However, I am having several problems: 1. The link to download G at rch_v40 does not work. Does anybody have a copy to email to me please? 2. Various guides offer different instructions for installing Ox in the correct place

please help.. I have estimate the value of parameter for AR,MA and fractional d.but I have problem on having the right command for forecasting ARFIMA model.please help...... -- View this message in context: http://r.789695.n4.nabble.com/how-to-make-ARFIMA-forecast-by-using-r-tp3869928p3869928.html Sent from the R help mailing list archive at Nabble.com.

Dear members, I am using arfima in an mclapply construction (from the parallel package): Browse[2]> LYG <- mclapply(LYGH, FUN = arfima, mc.cores = detectCores()) ^C Browse[2]> LYG <- mclapply(LYGH[1:10], FUN = arfima, mc.cores = detectCores()) ^C Browse[2]> LYG <- mclapply(LYGH[1:2], FUN = arfima, mc.cores = detectCores()) ^C You can see that I am

We have tried Asterisk 1.0.9 on FC4 and have never been able to get CAPI (with Fritz card, fcpci) to work properly. Apart from that Asterisk works fine in switching internal calls. But it's useless if we can't make outgoing calls on our ISDN line. We are considering abandoning FC4 for Debian or SuSe. What is the general concensus on the best Linux to run Asterisk with CAPI? /Why Tea

Dear members, I am using pbmcapply to parellise my code. But the following code doesn't work: > LYG <- pbmclapply(LYGH,FUN = arfima,mc.cores = 2,mc.preschedule = FALSE) | | 0%, ETA NA^ It just hangs. But the

I am trying to read an ftable using read.ftable, but I get the following error message: > jobSatTable <- read.ftable("http://definetti.uark.edu/~gpetris/stat5333/jobSatisfaction.dat",skip=2) Error in seek(file, where = 0) : no applicable method for "seek" In addition: Warning messages: 1: no non-missing arguments to max; returning -Inf 2: no non-missing arguments to

Dear All, I have a database of 200 observations named myD. In the dataframe there are a column named code (with codes varying from 1 to 77), a column named "prevalence" with some quantitative measurements are given and an column named Pr_mean, with no values. I would like to set a cycle to compute the average of prevalence values for each different code and store the averages under the

Hola, ¿hay alguna librería que sirva para modelizar la inflación utilizando un modelo modelo fraccionalmente integrados ARFIMA-STVGARCH? Saludos, Sebastián.

Can this be re-implemented to run faster (without the loop) ? r <- list() n = nrow(prices) for (i in (w+1):n) { window <- prices[(i-w):(i-1),] if (prices[i,]$settle > max(window$high)) r <- append(r, 1) else if (prices[i,]$settle < min(window$low)) r <- append(r, -1)

Hi - I read through dse package manual a bit. I'm not quite certain how I can use it to estimate a time varying coefficient regression model? I might pick up an inappropriate package. Any suggestion would be greatly appreciated. Thank you. rh

Hello! I used the function fracdiff(dn, nar=1, nma=1) and got the values of d, ar and ma coefficients. Also another coefficients were get under fdGPH, fdSperio. How could I get the forecasts in these models? Thank you very much [[alternative HTML version deleted]]