similar to: arima function

Thanks a lot to everybody that helped me out with this. Conclusions: (1) In order to edit arima in R: >fix(arima) or alternatively: >arima<-edit(arima) (2) This is not contained in the "Introduction to R" manual. (3) A "productive" fix of arima is attached (arma coefficients printed out and error catched so that it doesn't halt parent loops to search for

hi, I want to add different colors on the background of a classical plot. Each color is associated to an interval of the x axis. example: the background is red on the interval [1,10], blue on [11,20]. I try the rect function but it isn't appropriate for the background. Can any one can help me please? best regards. --------------------------------- [[alternative

I have a question on scope/reference/value type of variables with 'R'. The issue cam up first when I look at the arima code. I see code like: myupARIMA <- function(mod, phi, theta) { . . . . mod } Then armafn <- function(p, trans) { . . . . Z <- upARIMA(mod, trarma[[1]], trarma[[2]]) . . . . res <- .Call(R_ARIMA_Like, x,

Hi, I have a problem with the ARIMA function, occuring when I set the parameter per (the period of SARIMA model) to a high value (see the exemple bellow). It seems that when per is high it takes a too large amount of memory to calculate the model and I have a memory storage error. But I don't really understand why it takes more memory when per is high, as there is the same number of

>>>>> Gebhardt, Albrecht <Albrecht.Gebhardt at aau.at> >>>>> on Sun, 19 Mar 2017 09:14:56 +0000 writes: > Hi, > the function outer can not apply a constant function as in the last line of the following example: >> xg <- 1:4 >> yg <- 1:4 >> fxyg <- outer(xg, yg, function(x,y) x*y) >> fconstg

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

Hi, the function outer can not apply a constant function as in the last line of the following example: > xg <- 1:4 > yg <- 1:4 > fxyg <- outer(xg, yg, function(x,y) x*y) > fconstg <- outer(xg, yg, function(x,y) 1.0) Error in outer(xg, yg, function(x, y) 1) : dims [product 16] do not match the length of object [1] Of course there are simpler ways to construct a constant

> Or is this a bad idea? I don't like the proposal. I have seen code like the following (in fact, I have written such code, where I had forgotten a function was not vectorized) where the error would have been discovered much later if outer() didn't catch it. > outer(1:3, 11:13, sum) Error in outer(1:3, 11:13, sum) : dims [product 9] do not match the length of object [1]

Hi, I have been using this site ( http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm) to help me with some ARIMA modelling in R. Unfortunately the methods mentioned do not appear to work with second order differencing; arima(*, 2, *). I have used some dummy data to illustrate my point. When I use the xreg=... method, the estimate of intercept is *way* off. This can be seen by the high s.e but I

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

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 On Mon, 21 May 2018, Yves Goergen wrote: >> May 21 10:36:14 mond2 dovecot: imap(yg@****.de): Debug: Namespace : >> /var/mail/virtual/****.de/yg/.Test doesn't exist yet, using default >> permissions >> May 21 10:36:14 mond2 dovecot: imap(yg@****.de): Debug: Namespace : Using >> permissions from

Another pathological test. arima does not crash for that series that crashes arma: arima(c(2.01, 2.22, 2.09, 2.17, 2.42), order=c(1,0,0)) However, arima crashes for this: arima(c(1.71, 1.78, 1.95, 1.59, 2.13), order=c(1,0,0)) arima seems pretty consistent in its crashing behaviour, since crashing for one series means crashing for all affine series: lets.crash.arima <- c(71, 78, 95, 59,

HI, there, When i use R, i tried to use function arima(), it complains: Error: couldn't find function "arima" But when I type "help.search("arima") ", I got arima() poped up.. arima(ts) ARIMA Modelling of Time Series arima.sim(ts) Simulate from an ARIMA Model arima0(ts) ARIMA Modelling of Time Series -- Preliminary

Dear all, When I run an arima(1,1,1) on an I(1) variable, e.g. 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 -- View this message in context:

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,

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

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

Hi all, First: I have a small line of code I'm applying to a variable which will be placed in a matrix table for latex output of accuracy measures: acc.aarima <- signif(accuracy(forecast(auto.arima(tix_ts, stepwise=FALSE), h=365)), digits=3). The time series referred to is univariate (daily counts from 12-10-2010 until 5-8-2010 (so not 2 full periods of data)), and I'm working on

Hello, I'm currently using auto.arima to verify the order of my arima model. I would like to use the model to conduct an intervention analysis. The problem is that, when I include a step function in auto.arima, by including a binary variable in "xreg", the arima order that auto.arima gives is different from when I don't include it. From my understanding, the

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