similar to: predict.Arima fails when x is not a time-series

Hello,guys: Recently, I am working on a seasonal ARIMA model. And I met some problem in the forecasting. Now I just want to know that How does R perform the predict procedure(the predict formula, the initial setting of errors,etc.)? I run the following commands and get the original code of the "predict" command, but I can't read it. Can anybody explain it to me? Thanks! saji from

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,

Hi all- I am trying to use the time function for ts class objects and do not understand the return value. I want to use it to set up a time trend in arima fits. It does not seem to return a correct linear sequence that matches the underlying time series. I am running: R version 2.8.1 (2008-12-22). For example: R> ## create a time series R> x <- rnorm(24) R> (xts <-

Dear all, Before I file this as a bug, I wanted to check if I didn't miss something. The help page of lag() says that the function returns a time series object. It actually does return something that looks like a ts object (the attribute tsp is set). But when using a vector, the class "ts" is not added to the result: > avec <- 1:10 > lag(avec) [1] 1 2 3 4 5 6 7 8

Hi, I'm using R for some arima models. In the past I used for arima models Rats and Tsp. Using the R arima function, I get only the statistics sigma^2 and log likelihood; with Rats and Tsp it is possible to obtain more statistics, such as R, R square, Durbin Watson, standard error, etc. Is it possible using R to have the statistics mentioned? My best regards,

I'd like to play with ARIMA models of stock prices, but I am a complete novice. Could some kind soul explain the relationship among packages "ts", "tseries", "dse", "dse2", and "fracdiff"? Are they 'competing' products or does one depend on another? Where would be the best place for a novice to begin? Thanks for any advice. PS. I

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

{all recent versions of R} Problem / design bug : diff.default() doesn't deal with "ts" objects properly, diff.ts() does, but that is only available from package "ts" where as the constructor function ts() is in base. It's not sufficient to just move diff.ts() to package base, since it relies itself on lag & lag.default which are also only in package ts.

This message is in MIME format --_=XFMail.1.1.p0.Linux:970825095458:252=_ Content-Type: text/plain; charset=us-ascii Here is a patch which fixes some problems with time series functions. Some examples of what goes wrong... > x <- ts(rnorm(100),start=1,deltat=2) > start(x) Error in ts[1] : object is not subsettable > end(x) Error in ts[2] : object is not subsettable > y <-

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

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

Hi *, Firstly, thank you so much for your time to read my email. I am currently interested in how to use R to predict time series from models fitted by ARIMA. The package I used is basic stats package, and the method I used is predict.Arima. What I know is that ARIMA parameters are estimated by Kalman Filter, but I have difficulty in understanding how exactly maximum likelihood (ML) estimator

>>>>> On Fri, 02 Jul 1999, Adrian Trapletti <Adrian.Trapletti@wu-wien.ac.at> said: Adrian> There seems to be a problem with plot.ts (R Version 0.64.2) > x<-cbind(1:10,2:11) > x<-as.ts(x) > plot(x) Adrian> Error: subscript (20) out of bounds, should be at most 10 This is definitely a bug --> CC: R-bugs ALL NOTE : This is *not* new

(As mentioned before, on R-devel) This code snippet shows what the Subject above says : > autoload("diff.ts", file="ts") > diff(ts(1:10,freq=2)) Error in tsp<-(*tmp*, value = c(xtsp[1] + lag * differences * xtsp[3], : invalid time series parameters specified > traceback() [1] "tsp<-(*tmp*, value = c(xtsp[1] + lag * differences * xtsp[3], " [2]

This message is in MIME format --_=XFMail.1.3.p0.Linux:990702182137:16900=_ Content-Type: text/plain; charset=us-ascii There was some discussion a while back on R-devel between Ross Ihaka, Paul Gilbert and myself about row subsetting in time series. I think the consensus was that "[.ts" should not try to coerce its result back to a time series object (which is underlying the problem

Hi, I have been using this website ( http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm ) to help me to fit ARIMA models to my data. At the moment I have two possible methods to use. Method 1 If I use arima(ts.data, order=c(1,2,0), xreg=1:length(ts.data)) then the wrong value for the intercept/mean is given (checked on SPSS and Minitab) and

Hi all, Forecasting from an arima model is easy with predict. But I can't manage to backcast : invent data from the model before the begining of the sample. The theory is easy : take your parameters, reverse your data, forecast, and then reverse the forecast I've tried to adapt the predict function to do that (i'm not sure that the statistical procedure is fine (with the residuals),

Dear All, R 1.9.1, Windows When copying and pasting a few lines from the 'predict.Arima' help, I get an error message: > data(lh) > predict(arima(lh, order = c(3,0,0)), n.ahead = 12) Error in eval(expr, envir, enclos) : Object "xreg" not found On the other hand, the following is OK: > data(lh) > predict(arima0(lh, order = c(3,0,0)), n.ahead = 12) $pred Time

Hello, I have a question regarding the predict command for ARIMA models. Why do I not have to give any input except the number of steps for the forecast? Which input value is used (in my case for “y(t)”)? Will the last parameter of the estimate vector be used (in my example “6”)? x <- c(1,2,3,4,5,6)model <- arima(x, order=c(1,0,0), method="ML")predict(model, n.ahead = 1) ->