similar to: I need detail information how to use "arima" code.

Hi, I am so novice in using R. I have some problems in my R script below which fits time series data and predict it one-step ahead. Here is a brief explanation on what I try to achieve Th16k is time series data (500 data points). The size of window for fitting and predicting is 50 (data points). As you can easily discover from my code, (fixed) window is moving/sliding to get next one-step

Hi This is my first post to this group, so apologies in advance if I get it wrong. I would like to know how the prediction for arima models works in R. I have a time series to which I fit an arima model, of varying AR and MA orders. I then use the predict function to project it forward. I have also written my own function to perform the prediction, but it gives different answers to Arima.predict

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

I've posted this before but have not been able to locate what I'm doing wrong. I cannot determine how the forecast is made using the estimated coefficients from a simple AR(2) model when there is an exogenous variable. Does anyone know what the problem is? The help file for arima doesn't show the model with any exogenous variables. I haven't been able to locate any documents

Dear R People: Here is some output from AR and ARIMA functions: > xb <- arima.sim(n=120,model=list(ar=0.85)) > xb.ar <- ar(xb) > xb.ar Call: ar(x = xb) Coefficients: 1 0.6642 Order selected 1 sigma^2 estimated as 1.094 > xb.arima <- arima(xb,order=c(1,0,0),include.mean=FALSE) > xb.arima Call: arima(x = xb, order = c(1, 0, 0), include.mean = FALSE)

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

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,

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 am using arima to develop a time series regression model, I am using arima b/c I have autocorrelated errors. Several of my independent variables are categorical and I have coded them as factors . When I run ARIMA I don't get any warning or error message, but I do not seem to get estimates for all the levels of the factor. Can/how does ARIMA handle factors in xreg? here is some example

Hello, I have used the arima.sim function to generate a lot of time series, but to day I got som results that I didn't quite understand. Generating two time series z0 and z1 as eps <- rnorm(n, sd=0.03) z0 <- arima.sim(list(ar=c(0.9)), n=n, innov=eps) and z1 <- arima.sim(list(ar=c(0.9)), n=n, sd=0.03), I would expect z0 and z1 to be qualitatively similar. However, with n=10 the

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)

R 1.6.2 on windows XP (and windows 2000) Dear Readers, I have to fit an ARIMA model to a blood pressure series to make predictions with it. But since I don't have a blood pressure data set yet I have to work with self made data. So I have created an AR( 2 ) series with the following code: series <- list() series$series <- arima.sim(n=2100, model=list(ar = c(0.6, 0.1)), sd=1) + 120

Hey, as a bloddy beginner in R I want to simulate a Arima (2,1,0) process with R. My problem is, that I don't know how to specify the AR. For a ARIMA(1,1,0) I use the following syntax: S <- arima.sim(list(order=c(1,1,0), ar=0.9), n=100). I think it is a stupid question with an easy answer. But when I google the only results are arima.sim for an ARIMA (1,1,0). Regards, Chris --

Hi, I am using the arima.sim function to generate some AR time series. However, the function does not seem to produce exactly the same time series when I specify the innov parameter. For example > r <- rnorm(300) > x <- arima.sim(300, model=list(order=c(1,0,0),ar=c(.96)), innov=r, n.start=10) > y <- arima.sim(300, model=list(order=c(1,0,0),ar=c(.96)), innov=r, n.start=10) >

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

I am puzzled by the warning message in the output below. It appears whether or not I fit the seasonal term (but the precise point of doing this was to fit what is effectively a second seasonal term). Is there some deep reason why AR parameters ("Warning message: some AR parameters were fixed: ...") should somehow intrude into the fitting of a model that has only MA terms? >

I am trying to simulate time series data for an ar(1) and ma(1) process. I want the error term to have either a t distribution with 1 degree of freedom or a normal distribution with mean=0 and sd=1. Here is my code: error.model=function(n){rnorm(n,mean=0, sd=1)} data<-arima.sim(model=list(ar=c(0.1)), n=1000, n.start=200, start.innov=rnorm(200,mean=0, sd=1), rand.gen=error.model ) data

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.

I have written before, but to no avail. I have found two minor problems with fitting time series models with R. The thing is, they may be solved with MINOR adjustments to the code. I have posted these problems with detailed examples here: http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm Briefly, the problems are (1) When fitting time series models when there is an AR term present, the

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: