similar to: Need help on ARIMA (time series analysis)

Hello, I am very new to R and Time Series. I need some help including R codes about the following issues. I' ll really appreciate any number of answers... # I have a time series data composed of 24 values: myinput = c(n1,n2...,n24); # In order to make a forecasting a, I use the following codes result1 = arima(ts(myinput),order = c(p,d,q),seasonal = list(order=c(P,D,Q))) result2 =

Dear all, In many papers regarding time series analysis of acquired data, the authors analyze 'marginal distribution' (i.e. marginal with respect to time) of their data by for example checking 'cdf heavy tail' hypothesis. For i.i.d data this is ok, but what if samples are correlated, nonstationary etc.? Are there limit theorems which for example allow us to claim that

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)

Dear All, I have a model to predict time series data for example: data(LakeHuron) Lake.fit <- arima(LakeHuron,order=c(1,0,1)) then the function predict() can be used for predicting future data with the model: LakeH.pred <- predict(Lake.fit,n.ahead=5) I can see the result LakeH.pred$pred and LakeH.pred$se but I did not see residual in predict function. If I have a model: [\ Z_t =

Hello to everyone! I am working on my final year project about multivariate time series. There are three variables in the multivariate time series model. I have a few questions: 1. I used acf and pacf plot and find my variables are nonstationary. But in adf.test() and pp.test(), the data are stationary. why? 2.I use VAR to get a model. y is the matrix of data set and I have made a once

I'd like to fit an ARMA(1,1) model to some data (Federal Reserve Bank interest rates) that looks like: ... 30JUN2006, 5.05 03JUL2006, 5.25 04JUL2006, N &lt;---- here! 05JUL2006, 5.25 ... One problem is that holidays have that "N" for their data. As a test, I tried fitting ARMA(1,1) with and without the holidays deleted. In other words, I fit the above data

Dear all: I'm a newbie and an amateur seeking help with forecasting the next in a non-stationary time series, with constraints of 1 (low) and 27 (high) applicable to all. What I need help with is the solution concept. The series has 439 observations as of last week. I'd like to analyze obs 1 - 30 (which are historical and therefore invariate), to solve for 31. The history: Obs 1

Hello, I using the fuction auto.arima() from package forecast to predict the values of p,d,q and P,D,Q. My problem is the execution time of this function, for example, a time series with 2323 values with seasonality to the week take over 8 hours to execute all the possibilities. I using a computer with Windows XP, a processor Intel Core2 Duo T7300 and 2Gb of RAM.

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,

Good morning to you all, Sorry for taking your time from your research and teaching schedules.   If you have a non-stationary univariate time Series data that has the transformation: Say; l.dat<-log (series) d.ldat<-diff (l.dat, differences=1) and you fit say arima model. predit.arima<-predict (fit.series, n.ahead=10, xregnew= (n+1) :( n+10)) How could I re-transform

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),

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

Dear all R users, I am really struggling to determine the most appropriate lag order of ARIMA model. My understanding is that, as for MA [q] model the auto correlation coeff vanishes after q lag, it says the MA order of a ARIMA model, and for a AR[p] model partial autocorrelation vanishes after p lags it helps to determine the AR lag. And most appropriate model choosed by this argument gives

I am new in R. I can simulate Arma, using Arima.sim However, I want to simulate an Arima Model. Say (1-B)Zt=5+(1-B)at. I do not know how to deal with 5 in this model. Can any one could help me? Thank you very much! Regards, -- View this message in context: http://www.nabble.com/simulate-arima-model-tp23239027p23239027.html Sent from the R help mailing list archive at Nabble.com.

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

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, Does anyone know a method for adding a linear/polynominal trend to a simulated arima model using the arima.sim function? Any help will be greatly appreciated. Cheers, Sam.

Good evening! I have a question regarding forecast package and time series analysis. My syntax: x<-c(253, 252, 275, 275, 272, 254, 272, 252, 249, 300, 244, 258, 255, 285, 301, 278, 279, 304, 275, 276, 313, 292, 302, 322, 281, 298, 305, 295, 286, 327, 286, 270, 289, 293, 287, 267, 267, 288, 304, 273, 264, 254, 263, 265, 278) library(forecast) arima(x, order=c(1,1,2),

Dear useRs, I want to simulate a time series (stationary; the distribution of values is skewed to the right; quite a few ARMA absolute standardized residuals above 2 - about 8% of them). Is this the right way to do it? #-------------------------------- load("rdtb") #the time series > summary(rdtb) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.11800 -0.65010 -0.09091

Could someone please give a brief explanation, or pointer to an explanation, of the following error: > arima(ts.growth, order = c(1,0,0),include.mean=T) Error in arima(ts.growth, order = c(1, 0, 0), include.mean = T) : non-stationary AR part from CSS and why it does not arise with > arima0(ts.growth, order = c(1,0,0)) Many thanks ____________________________ Dr. Daniel P. Bebber