similar to: Difference between arima(1, 1, 1) for y and arima(1, 0, 1) for diff(y)

Dear all, When I run an arima(1,1,1) on an I(1) variable, y, I get different estimates to when I first difference the variable myself, e.g y2<-diff(y), and then run arima(1,0,1) on y2. Shouldn't these two approaches give the same output? Any help will be much appreciated. george

Full_Name: Allan McRae Version: 1.6.0 OS: Win 2000 P Submission from: (NULL) (129.215.190.229) When using predict.Arima in library ts(), it appears differencing is only accounted for in the first step of prediction and so any trend is not apparent in the predictions. The example shows the difference between the predictions of an arima(1,1,1) model and the backtransformed predictions of an

Hi, I am trying to understand exactly what xreg does in arima. The documentation for xreg says:"xreg Optionally, a vector or matrix of external regressors, which must have the same number of rows as x." What does this mean with regard to the action of xreg in arima? Apparently somehow xreg made the following two arima fit equivalent in R: arima(x, order=c(1,1,1), xreg=1:length(x)) is

I am having some issues with differencing using auto.arima when also specifying an xreg dataframe. The xreg dataframe contains dummy variables that specify time periods that had a promotion running. When I model diff(y) with order (1,0,1), the coefficients for these dummy variables are very different than when I model y with order=(1,1,1). I think when modeling diff(y) the coefficients

Hi, I have the following code from Splus that I'd like to migrate to R. So far, the only problem is the arima.filt function. This function allows me to filter an existing time-series through a previously estimated arima model, and obtain the residuals for further use. Here's the Splus code: # x is the estimation time series, new.infl is a timeseries that contains new information # a.mle

Dear R-helpers. Would appreciate if someone can explain the warning messages below, after auto.arima. I couldn't find any clue in the archived help. Also, how do I retrieve the AICs of each tried model in auto.arima? The purposes are (1) to output to a text file, and (2) to find the 2nd best model by finding 2nd lowest AIC instead of eyeballing thru the value at the console

Hello , I have estimated the following model, a sarima: p=9 d=1 q=2 P=0 D=1 Q=1 S=12 In R 2.12.2 Call: arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), optim.control = list(reltol = tol)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3152 0.8762 -0.4413 0.0152 0.1500 0.0001 -0.0413 -0.1811

Dear List, A posting to R-Help exposed this problem with the print method for objects of class Arima: > set.seed(1) > x <- arima.sim(n = 100, list(ar = 0.8897, ma = -0.2279)) > mod <- arima(x, order = c(1,0,1)) > coefs <- coef(mod) > mod2 <- arima(x, order = c(1,0,1), fixed = coefs) > mod2 Call: arima(x = x, order = c(1, 0, 1), fixed = coefs) Coefficients: Error

Hello all, I use arima to fit the model with fit <- arima(y, order = c(1,0,1), xreg = list.indep, include.mean = TRUE) and would like to use predict() to forecast: chn.forecast <- rep(0,times=num.record) chn.forecast[1] <- y[1] for (j in 2:num.record){ indep <- c(aa=chn.forecast[j-1], list.indep[j,2:num.indep]) # this is the newxreg in the

To whom it may concern: I am trying to better understand the functionality of 'R' when making arima predictions to avoid any "Black Box" disadvantages. I'm fitting a seasonal arima model using the following command (having already loaded 'stat' package). arimaSeason <- arima(Data,order=c(1,0,1),seasonal=list(order=c(1,0,1),period=12)) I can then generate

Hi, I am trying to forecast a model using predict.Arima I found arima model for a data set: x={x1,x2,x3,...,x(t)} arima_model = arima(x,order=c(1,0,1)) I am forecasting the next N lags using predict: arima_pred = predict(arima_model,n.ahead = N, se.fit=T) If I have one more point in my series, let's say x(t+1). I do not want to recalibrate themodel, I just want to forecast the next N-1

dear all, Is it possible to extract the response vector from a fitted arima object? For instance in glm it is allowed, by: obj.glm<-glm(y~x) obj.glm$y #the response vector In arima I can't find it: obj.arima<-arima(y, order=c(1,0,1)) #say names(obj.arima) doesn't seem to include the response. Am I wrong? Many thanks for your help, best, vito

Hi, I'm using the function arima() from the ts package. when the function gives me the output I can see the s.e. of the coefficients. However I cannot find a way to collect them in a object estimate<-arima(x, order=c(1,0,1)) estimate$se does not work in fact str(estimate) does not contain $se. How is it possible? The function compute and print that value but it is not the resulting

Here is my problem: Autoregressive models are very interesting in forecasting consumptions (eg water, gas etc). Generally time series of this type have a long history with relatively simple patterns and can be useful to add external regressors for calendar events (holydays, vacations etc). arima() is a very powerful function but kalman filter is very slow (and I foun difficulties of estimation)

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, the function makeARIMA(), designed to construct some state space representation of an ARIMA model, uses a C function called getQ0, which can be found at the end of arima.c in R source files (library stats). getQ0 takes two arguments, phi and theta, and returns the covariance matrix of the state prediction error at time zero. The reference for getQ0 (cited by help(arima)) is:

sseq <- c(1, seq(5, 120, by = 5)) for(i in 1:length(sseq)){ assign(paste("arima", i, sep=""), arima0(data.ts[sseq[i]:(sseq[i]+115)], order=c(1,1,1))) } pred1 = predict(arima1, n.ahead = 5, se.fit = TRUE)$pred how do I traverse the arima models so I repeat the above prediction procedure(bold) on all arima models. Also, how do i automatically create one huge vector of the

Hi, i'm beginner in Bayesian methods, I'm reading the documentation about dlm package and kalman filters, I'm looking for a example of transformation of ARIMA in a state space equivalent to use the dlm package and calcualte the hyperparameters. Someone can help me about it?. If it's possible with a arima(1,0,1) example, or more complex model. While I have more examples best for me.

Hi users. I'm new user in R. I'm workiing with Time series and I would like to know how can I do to undo the command DIFF(X), for exemple: If I have the model: m=arima(X, order=c(0,1,1), seasonal=list(order=c(0,0,1))) (note that have d=1 one difference), to find, in the same scale, the original numbers (like one "unDiff"), after the forecast, I need to develop some function or in

Hello, I need to know how to import ARIMA coefficients. I already determined the coefficients of the model with other software, but now i need to do the forecast in R. For Example: I have a time series named x and i have fitted an ARIMA(1,0,1) (with other software) AR coef = -.172295 MA coef = .960043 (i know that this is not a good model, it's just an example) I try to