similar to: What's the predict procedure of ARIMA in R?

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

When I run predict.Arima in my code, I get warnings like: Warning message: In cbind(intercept = rep(1, n), xreg) : number of rows of result is not a multiple of vector length (arg 1) I think this is because I'm not running predict.Arima in the same environment that I did the fit, so the data object used in the fit is no longer present. Looking at the predict.Arima source,

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

On 2025-01-02 9:04 a.m., Norbert Kuder wrote: > Hello all, > > I am running R version 4.4.2 (2024-10-31 ucrt) on Windows 10 x64, and > noticed something that might be a minor bug (or at least inconsistent code) > in the stats/arima.R package. > I have found: > 1. A missing stop() call at line 69: > if (length(order) == 3) seasonal <- list(order = seasonal) else

On 2025-01-02 11:20 a.m., Duncan Murdoch wrote: > On 2025-01-02 9:04 a.m., Norbert Kuder wrote: >> Hello all, >> >> I am running R version 4.4.2 (2024-10-31 ucrt) on Windows 10 x64, and >> noticed something that might be a minor bug (or at least inconsistent code) >> in the stats/arima.R package. >> I have found: >> 1. A missing stop() call at line 69:

>>>>> Duncan Murdoch >>>>> on Thu, 2 Jan 2025 11:28:45 -0500 writes: > On 2025-01-02 11:20 a.m., Duncan Murdoch wrote: >> On 2025-01-02 9:04 a.m., Norbert Kuder wrote: >>> Hello all, >>> >>> I am running R version 4.4.2 (2024-10-31 ucrt) on Windows 10 x64, and >>> noticed something that might

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,

Dear all, I am fitting an arimax (arima with some extra explanatory variables) model to a time series. Say, I have a Y (dependent variable) and an X (explanatory). Y is 100 observations (time series) and X is 100 + 20 (20 to use for the forecast horizon). I can not make xreg work with the forecast function for an arima fit. The "predict" function seems to be working but the

I am working on a script that takes numeric performance indicators and runs them against a series of regressors (dummy regressors, yes\no stuff via 0 and 1, e.g. Was is Christmas this week 0=no, 1=yes). The script is as follows (Written as a function): -- Begin Script -- doEnv <- function(HOUR,ENVNAME,REPORTNAME) { library(RODBC) library(forecast) library("geneplotter")

Dear all, I just have a short question regarding the forecasting of ARIMA models with external regressors. I tried to program a ARX(1) model arx.mod <- arima(reihe.lern, order = c(1, 0, 0), seasonal = list(order = c(0, 0, 0), period = 52), xreg = lern.design, include.mean = TRUE) for which I need to estimate the next (105th) value. Xreg=lern.design is - at this time - 104 rows long. I

to clean up some code I would like to make a list of arbitrary length to store?various objects for use in a loop sample code: ############ BEGIN SAMPLE ############## # You can see the need for a loop already linearModel1=lm(modelSource ~ .,mcReg) linearModel2=step(linearModel1) linearModel3=lm(modelSource ~ .-1,mcReg) linearModel4=step(linearModel3) #custom linearModel5=lm(modelSource ~ .

I'm trying to do the following out of sample regression with autoregressive terms and additional x variables: y(t+1)=const+B(L)*y(t)+C(1)*x_1(t)...+C(K)*x_K(t) where: B(L) = lag polynom. for AR terms C(1..K) = are the coeffs. on K exogenous variables that have only 1 lag Question 1: ----------- Suppose I use arima to fit the model:

Hello everybody! I have a problem when I try to perform a forecast of an ARIMA model produced by an auto.arima function. Here is what I'm doing: c<-auto.arima(fil[[1]],start.p=0,start.q=0,start.P=0,start.Q=0,stepwise=TRUE,stationary=FALSE,trace=TRUE) # fil[[1]] is time series of monthly data ARIMA(0,0,0)(0,1,0)[12] with drift : 1725.272 ARIMA(0,0,0)(0,1,0)[12] with drift

Hi R Users, I'm fairly new to R (about 3 months use thus far.) I wanting to use the arimax function from the TSA library to incorporate some exogenous inputs into the basic underllying arima model.Then with that newly model of type arimax, I would like to make a prediction. To avoid being bogged down with issues specific to my own work, I would like to refer to readers to the example

Hi all there I am enjoying R since 2 weeks and I come to my first deadlock, il am trying to use predict.Arima in the ts package. I get a "Error in cbind(...) : cannot create a matrix from these types" -- Start R session ----------------------------------------------------- > fitdiv <- arima(data, c(2, 0, 3), xreg = y ) ; print(fitdiv) Call: arima(x = data, order = c(2, 0, 3),

Hi all there I am enjoying R since 2 weeks and I come to my first deadlock, il am trying to use predict.Arima in the ts package. I get a "Error in cbind(...) : cannot create a matrix from these types" -- Start R session ----------------------------------------------------- > fitdiv <- arima(data, c(2, 0, 3), xreg = y ) ; print(fitdiv) Call: arima(x = data, order = c(2, 0, 3),

Hello all, I''m having fun again with the arima function. This time I read in: http://www.stat.pitt.edu/stoffer/tsa2/R_time_series_quick_fix.htm <<It has recently been suggested (by a reliable source) that using xreg in arima() does NOT fit an ARMAX model [insert slap head icon here]. This will be investigated as soon as time permits.>> (by R.H. Shumway & D.S. Stoffer)

Hello everyone, Hope you all are doing great! I have been fitting arima models and performing forecasts pretty straightforwardly in R. However, I wanted to add a couple of regressors to the arima model to see if it could improve the accuracy of the forecasts but have had a hard time trying to do so. I used the following R function: arima(x, order = c(0, 0, 0), seasonal = list(order = c(0, 0,

Hello R users, Hope everyone is doing great. I have a dataset that is in .csv format and consists of two columns: one named Period (which contains dates in the format yyyy_mm) and goes from 1995_10 to 2007_09 and the second column named pcumsdry which is a volumetric measure and has been formatted as numeric without any commas or decimals. I imported the dataset as pauldataset and made use of

Good afternoon! I'm trying to learn time series but i have a bit of of a problem using R packages for this. 1. > LakeHuron > sample(500:600, 98) > sample(500:600, 98)->t > fit<-arima(LakeHuron, order=c(2,1,1), xreg=t) > fit > predict(fit, n.ahead=1, newxreg=t) Now, my problem is this: is it ok to use the same t in predict function or should my newxreg contain 99