similar to: arima() in ts

I've been trying to get comfy with arima() and associated functions in the ts() package. I'm thinking seriously about using this package, and R generally, in a 4th year intro time series course that I'm teaching this autumn. I have a couple of questions about arima: (1) The help file says that residuals component of the value returned by arima() consists of the

Hi all, I want to calculate the residuals of an ARIMA fit to a time series using the same calculation method as in arima() in the ts package, BUT I want to be able to specify fixed values for ALL the parameters in the ARIMA model. The "fixed = ..." argument in the arima() command appears to require at least one parameter to be variable. Does anyone know a way around this?

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

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 fitting an arima model to some time series X. When I was comparing the fitted values of the model to the true time series I realized that the true time series lags one time step behind the fitted values of the arima model. And this is the case for any model. When I did a simple linear regression using lm to check, I also find the same results, that the true series lags behind the

Hi I'm trying to fit arima models with the arima() function and I have two questions. ###### ##1. ## ###### I have n observations for my time series. Now, no matter what arima(p,d,q)- model I fit, I always get n residuals. How is that possible? For example: If I try this out myself on an AR(1) and calculate the fitted values from the estimated coefficients I can calculate n-1 residuals.

This question is surely trivial, sorry. I'm afraid I'm misunterpreting the information I got with the documentation, and I'm a little bit confused. I'm just an engineer with some little skills in statistics. Well, I have a time series - 600 days long - with some weekly periodicity inside. So far, so good. Well, if I define the time series with, say : a <- ts(b,

Hi, Although my doubt is pretty,as i m not from stats background i am not sure how to proceed on this. Currently i am doing a forecasting.I used ARIMA to forecast and time series was volatile i used garchFit for residuals. How to use the output of Garch to correct the forecasted values from ARIMA. Here is my code: ###delta is the data fit<-arima(delta,order=c(2,,0,1)) fit.res <-

Hi all, I have two time series that I would like to correlate but as they are autocorrelated, I am "pre-whitening" them first by fitting ARIMA models, then correlating their residuals....as described in https://onlinecourses.science.psu.edu/stat510/?q=node/75 However, http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm discusses some issues with ARIMA in R. In particular, for issue 2, if

Hi all! I want to fit a time series with 17376 values by using the arima() function. If I extract the residuals from the fitted model there are values for the residuals 1 to 710 but the residuals 711 to 17376 have the value NA. Does anybody know what the problem could be? Is the function arima() restricted to a maximum length of the underlying time series? Besides, is there a better possibility

Dear list and Martin, I'm testing different approaches to fit an electricity demand time series and come upon the fracdiff package (v 1.3-1) for fitting fractional ARIMA models. The following questions are motivated by this package. 1. Despite having a help page, the residuals and fitted functions don't seem to have implementation, or did i miss something obvious? Alternatively, having a

Hi I have fitted an arima model to a monthly series of length 113. I have use fit <- arima(x.ts, order = c(1,0,0),seas = list(order = c(1,2,1),12, method = "CSS") I'm a little confused because the series of residual has the first 46 values equal to 0. From there the residuals are approximately while noise, numbers have a mean close to 0 and a variance value. What could be

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

> From: "Krassimir Krastev" <krastev at fas.harvard.edu> > To: "R-help" <r-help at stat.math.ethz.ch> > Date: Tue, 29 Feb 2000 11:56:49 -0500 > X-Priority: 3 (Normal) > X-MSMail-Priority: Normal > Importance: Normal > X-MimeOLE: Produced By Microsoft MimeOLE V5.00.2919.6600 > > Does anyone know of any comprehensive literature (like a

David Stoffer describes some challenges with R's output when fitting ARIMA models for different orders (see Issue 2 at http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm). R doesn't fit an intercept in the model if there is any differencing. David describes a workaround using the xreg parameter to force R to calculate an intercept. Assume I have a variable y and 3 explanatory variables a,

Hi, All, This is my program ts1.sim <- arima.sim(list(order = c(1,1,0), ar = c(0.7)), n = 200) ts2.sim <- arima.sim(list(order = c(1,1,0), ar = c(0.5)), n = 200) tdata<-ts(c(ts1.sim[-1],ts2.sim[-1])) tre<-c(rep(0,200),rep(1,200)) gender<-rbinom(400,1,.5) x<-matrix(0,2,400) x[1,]<-tre x[2,]<-gender fit <- arima(tdata, c(1, 1, 0), method = "CSS",xreg=t(x))

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),

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

Dear R users, Please let me know how to plot the forecast in such a model: First I do it simple with ARIMA model that works ok with the codes provided to me at the lecture: arima<-arima(HCPIlong, order=c(1,1,0)) arima.predict<-predict(arima, n.ahead= 5 ) ts.plot(HCPIlong,arima.predict$pred,lty=1:2, main="Forecast of HCPI") But I need to include the additional variable in my

I'm trying to get more info about seasonal parameter of arima(ts). Can someone explain on its usage? I'm also interested in building a model for weekly sales for an item with covariates like consumer confidence index, promotion flag etc. Which package has functions to model and do predictions for this kind of time series data? Thanks, Gopal [[alternative HTML version deleted]]