similar to: Structural TS and recursive estimation

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

This is the output of ./buidpkg.sh of openssh-3.1p1 on "SunOS 5.6 Generic_105181-17 sun4m sparc SUNW,SPARCstation-20" Building pkginfo file... Building prototype file... Building package.. ## Building pkgmap from package prototype file. ## Processing pkginfo file. pkgmk: ERROR: parameter <VERSION> cannot be null ## Packaging was not successful. pkgtrans: ERROR: unable to complete

Dear all, I'd like to discuss about a possible bug in function StructTS of stats package. It seems that the function returns wrong value of the log-likelihood, as the added constant to the relevant part of the log-likelihood is misspecified. Here is an simple example: > data(Nile) > fit <- StructTS(Nile, type = "level") > fit$loglik [1] -367.5194 When computing the

I am trying to use KalmanSmooth to smooth a time series fitted by arima (and with missing values), but the $smooth component of the output baffles me. Look at the following example: testts <- arima.sim(list(ar=0.9),n=100) testts[6:14] <- NA testmod <- arima(testts, c(1,0,0)) testsmooth <- KalmanSmooth(testts, testmod$model) par(mfrow=c(2,1)) plot(testsmooth$smooth,

Hi, Few days ago I posted a question to r-sig-finance, which I thought would be an easy one. To my surprise I have received no replies, which makes me think that it is either harder than I thought, or that it makes no sense. I am reposting the message (with some modifications) on the R-help in a hope to get some leads, suggestions for alternatives, etc. My apologies to those who had seen this on

>From the help document on KalmanLike, KalmanRun, etc., I see the linear Gaussian state space model is a <- T a + R e y = Z' a + eta following the book of Durbin and Koopman. In practice, it is useful to run Kalman filtering/smoothing/forecasting with exogenous factor: a <- T a + L b + R e y = Z' a + M b + eta where b is some known vector (a function of time). Some other

Je serai absent(e) du 12/11/2005 au 16/11/2005. Je r??pondrai ?? votre message d??s mon retour. I will be out of the office from 14-MAR-2005 until 18-MAR-2005 I will reply to your message on my return. Julien Ruiz ---------------- L'acces immediat aux meilleurs tarifs Air France et au billet electronique sur http://www.airfrance.com For immediate access to the best Air France fares

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

To whomever it may concern, I'm a young Industrial Engineer working on Senior Design at Georgia Tech and have found the StructTS method to be excellent for the training set for my forecasting project. There's only one problem: I don't actually understand what a Structural Time Series IS. I've looked up resources on it, and get that essentially you're dividing the Time

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)

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

I did a regression with ARMA errors using arima0 with ari<-arima0(y,order=c(2,0,2),xreg=reg1,delta=-1) or ari<-arima0(y,order=c(2,0,2),xreg=reg1) where reg1 is the matrix of the regressors and when I see diag(ari$var.coef) I get negative terms. Do you know what this mean ? I try to change transform.pars to 0 or 1 but this crash R on Windows. Is it possible to test the significativity

Hi, i'm beginner in Bayesian methods, I'm reading the documentation about dlm package and kalman filters, I'm looking for a example of transformation of ARIMA in a state space equivalent to use the dlm package and calcualte the hyperparameters. Someone can help me about it?. If it's possible with a arima(1,0,1) example, or more complex model. While I have more examples best for me.

I'm searching a method to estimate the hyper-parameters in arima models. I'm reading about r-inla package, but in the examples section only talk about the AR part of the arima, but i need help about the MA part too. I'm beginner in Bayesian methods, I'm reading the documentation about dlm package and kalman filters, but the computacional cost of inla i think is better, but only

Hello all, I have been using R's time series capabilities to perform analysis for quite some time now and I am having some questions regarding its reliability. In several cases I have had substantial disagreement between R and other packages (such as gretl and the commercial EViews package). I have just encountered another problem and thought I'd post it to the list. In this case,

Hi, I'm dealing with time series. I usually use stl() to estimate trend, stagionality and residuals. I test for normality of residuals using shapiro.test(), but I can't test for autocorrelation and heteroskedasticity. Is there a way to perform Durbin-Watson test and Breusch-Pagan test (or other simalar tests) for time series? I find dwtest() and bptest() in the package lmtest, but it

https://bugs.freedesktop.org/show_bug.cgi?id=51207 Bug #: 51207 Summary: Background corruption in Firefox w/ cairo Classification: Unclassified Product: Mesa Version: 8.0 Platform: x86-64 (AMD64) OS/Version: Linux (All) Status: NEW Severity: normal Priority: medium Component:

Hello, Another French student in the rsync mailing list. I have been working on rsync this year for a documentation project for school and I would like to give some comment about rsync block size optimization first, and then to submit a way to make rsync choose by itself the optimal blocksize when updating a large number of files. Well, the first comment: during my work, I wanted to verify

Anyone know how to esimate parameters in the system: x[k]=Ax[k-1]+ B + Gv[k-1] y[k]=x[k]+Du[k]+Hw[k] a system with exogenous u[k] in the measurement eq., v,w are iid, both eq. are gaussian. Thanks, Oyvind --------------------------------- [[alternative HTML version deleted]]

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