similar to: arima.sim help

Hello, I have used the arima.sim function to generate a lot of time series, but to day I got som results that I didn't quite understand. Generating two time series z0 and z1 as eps <- rnorm(n, sd=0.03) z0 <- arima.sim(list(ar=c(0.9)), n=n, innov=eps) and z1 <- arima.sim(list(ar=c(0.9)), n=n, sd=0.03), I would expect z0 and z1 to be qualitatively similar. However, with n=10 the

Hi, I have been using arima.sim from the stats package recently, and I'm wondering why I get different results when using what seem to be the same parameters. For example, I've given examples of three different ways to run arima.sim with what I believe are the same parameters. It's my understanding from the R documentation that rnorm is the default function for rand.gen if not

Hi, I am using the arima.sim function to generate some AR time series. However, the function does not seem to produce exactly the same time series when I specify the innov parameter. For example > r <- rnorm(300) > x <- arima.sim(300, model=list(order=c(1,0,0),ar=c(.96)), innov=r, n.start=10) > y <- arima.sim(300, model=list(order=c(1,0,0),ar=c(.96)), innov=r, n.start=10) >

Hi Everyone, I'm having a problem with arima.sim. Namely specifying inital values for the series. If I generate a random walk > vs = rnorm(100,0,1) > xs = cumsum(vs) and fit an ARIMA(1,0,0) to it > xarima = arima(xs,order=c(1,0,0)) > xarima Call: arima(x = xs, order = c(1, 0, 0)) Coefficients: ar1 intercept 0.9895 8.6341 s.e. 0.0106 6.1869 I should

Hello, I have a question about function arima.sim I tried to somulate a AR(1) process, with no innovation, no error term. I used this code: library(forecast) e=rnorm(100,mean=0,sd=0) series=arima.sim(model=list(ar=0.75),n=100,innov=e)+20 Then I tried to applicate ti this series auto.arima function: mod1<-auto.arima(series,stepwise=FALSE,trace=TRUE,ic='aicc') The best model returned

Apologies for thickness - I'm sure that this operates as documented and with good reason. However... My understanding of arima.sim() is obviously imperfect. In the example below I assume that x1 and x2 are similar white noise processes with a mean of 5 and a standard deviation of 1. I thought x3 should be an AR1 process but still have a mean of 5 and a sd of 1. Why does x3 have a mean of ~7?

Dear Users, I follow Andreas idea to simulate an ar(1) model with a new kind of innovation process. The new argument rand.gen, for the arima.sim function, I'm trying to generate as: tGarchGen <- function(a, b, c) { # must return a vector of random deviates (eta(t)) for (t in 1:100){ z(t) <- c+a*(eta(t)^2)+b*z(t-1) eta(t) <-rt(100, 5)*sqrt(z(t)) #rt is the R random

Hi This is my first post to this group, so apologies in advance if I get it wrong. I would like to know how the prediction for arima models works in R. I have a time series to which I fit an arima model, of varying AR and MA orders. I then use the predict function to project it forward. I have also written my own function to perform the prediction, but it gives different answers to Arima.predict

Hello, here is another question, how do I generate random walk models in R? Basically, I need an AR(1) model with the phi^1 value equal to 1: Yt = c + Yt-1 + E where E is random white noise. I tried using the arima.sim command: arima.sim(list(ar=c(1)), n = 1000, rand.gen = rnorm) but got this error since the model I am generating is not stationary: Error in arima.sim(list(ar = c(1)), n =

I have been trying to simulate from a time series with trend but I don't see how to include the trend in the arima.sim() call. The following code illustrates the problem: # Begin demonstration program x <- c(0.168766559, 0.186874000, 0.156710548, 0.151809531, 0.144638812, 0.142106888, 0.140961714, 0.134054659, 0.138722419, 0.134037018, 0.122829846, 0.120188714,

Dear Lillian, I tried to estimate parameters for time series regression using time series bootstrapping as described on page 434 in Davison & Hinkley (1997) - bootstrap methods and their application. This approach is based on an AR process (ARIMA model) with a regression term (compare also with page 414 in Venable & Ripley (2002) - modern applied statistics with S) I rewrote the code

In some time series simulations I'm doing, I occasionally want the model to be ``white noise'', i.e. no model at all. I thought it would be nice if I could fit this into the arima.sim() context, without making an exceptional case. I.e. one ***could*** do something to the effect if(length(model)==0) x <- rnorm(n) else x <- arima.sim(model,n) but it would be more suave if one

Hey, as a bloddy beginner in R I want to simulate a Arima (2,1,0) process with R. My problem is, that I don't know how to specify the AR. For a ARIMA(1,1,0) I use the following syntax: S <- arima.sim(list(order=c(1,1,0), ar=0.9), n=100). I think it is a stupid question with an easy answer. But when I google the only results are arima.sim for an ARIMA (1,1,0). Regards, Chris --

Full_Name: Gang Liang Version: 1.7.1 OS: Debian/Woody Submission from: (NULL) (192.6.19.190) > print(arima.sim(list(ar=.3,order=c(1,1,1)), 30)) [1] 0.00000000 0.10734243 0.02907301 -1.23441659 -0.98819317 -2.82731975 [7] -2.69052512 -4.22884756 -5.02820635 -5.41514613 -6.20486350 -7.01040649 [13] -6.78121289 -5.41111810 -4.96338053 -5.42395408 -6.22741444 -5.75228153 [19] -6.07346580

Hi, I tried to simulate an ARIMA model by using arima.sim(), say arima.sim(n=100,list(order=c(1,0,1),ar=0.6,ma=0.9,sd=1), but the acf and pacf of simulated data using acf() and pacf() are so much different from the theoritcal acf and pacf. For instance, in my case, ar=0.6 and ma=0.9, so the acf for all lags should be greater than 0 based on the theoritical calculation, but the acf of simulated

Hi all. I have a periodiv arma model and I want to simulate it. In S-plus, the following works for me: phi <- 0.9 theta <- 0 p <- 1 # period model <- list(ar=phi, ma=theta, period=p) Yt <- arima.sim(model, n=250) How do I do something like this "period=12" in R? I read help(arima.sim) but it doesn't tell. Thanks! Philip.

hi, I am trying to fit an ARIMA model to some time series data, I have used differencing to make the data stationary. dailyibm is the data I am using, could someone please help out in identifying the coding as I can't seem to identify the problem many thanks > fit.ma <- arima.sim(dailyibm - mean(dailyibm), model=list(order=c(0,1,0)),n = 3333) Error in inherits(x,

Hi, Before I re-invent the wheel, is there a function in R similar to S+'s arima.sim, i.e., a function that simulates arima processes. ts and tseries packages don't seem to have such function, but I may have overlooked it. Thank you for your time, Alvaro Novo R Version 1.1.1 SuSE 6.4 Linux KDE 2.0 Beta 5 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Dear colleagues, I am relatively new to R and time series and so I am experiencing difficulties in interpreting the output of "arima" in MA models (but not in AR models). I cannot make sense of the 1st innovations returned by "arima". In an AR(1) model I expect data[t]=phi1*data[t-1]+a[t] and in a MA(1) model data[t]=a[t]+theta1*a[t-1]. My interpretation from R-help is

Dear useRs, I am trying to model a time series with a transfer function. I think it can be put into the ARMA framework, and estimated with the 'arima' function (and others have made similar comments on this list). I have tried to do that, but the results have so far been disappointing. Maybe I am trying to make 'arima' do something it can't... The data are time series of