search for: kalmanforecast

Hi R-Experts, Currently I'm using an univariate time series in which I'm going to apply KalmanLike(),KalmanForecast (),KalmanSmooth(), KalmanRun(). For I use it before makeARIMA () but I don't understand and i don't know to include the seasonal coefficients. Can anyone help me citing a suitable example? Thanks in advance. ------------------------------------------ SUMANTA BASAK. ----------------...

Hi My name is Vladimir Samaj. I am a student of Univerzity of Zilina. I am trying to implement Kalman Filter into my school work. I have some problems with understanding of R version of Kalman Filter in package stats( functions KalmanLike, KalmanRun, KalmanSmooth,KalmanForecast). 1) Can you tell me how are you seting the initial values of state vector in Kalman Filter? Are you using some method? 2) I have fond function StructTS in stats package. I dont understand, how exactly, are you computing(what method are you using) fitted values which are the output of this functi...

...<-rep(1,100) after the definition, to check that). Then i look at the code, with getS3method("predict","Arima"). And i get even more confused (!) : where does data play a role in the function ? residuals are loaded into rsd, but this variable is not used after... I looked at KalmanForecast and at the C code of KalmanFore, but it did not help me understand what was going on. thanks Franck A. btw, it has nothing to do with it, but i've done some stuff on time series (filtering with Hodrick prescott or Baxter King, for instance) that you can find on http://arnaud.ensae.net [[al...

Dear R users, the preliminary program for the 3rd international workshop on 'Distributed Statistical Computing' (DSC 2003) is now available at http://www.ci.tuwien.ac.at/Conferences/DSC-2003/ The workshop will take place at the Technische Universit?t Wien in Vienna, Austria from March 20-22. The deadline for registrations is March 14, but the deadline for early registration ends on

...e="BSM". I have two questions: 1-in the "fitted" value of the object, it seems to me that for getting back fitted values, one have to add level, slope and seasonal (or "level" contains already the two other ones) 2-i am really circumspect about the results of "KalmanForecast" because I get some previsions which seem to be not seasonal. Does the forecast concern only the "level" (and in this case, how is it possible to get real prediction as the seasonality and the slope are not forecasted?)?? Thanks in advance for any help, with my best, erik. PS: pa...

...)) warning("MA part of model is not invertible") } if (arma[4] > 0) { ma <- coefs[sum(arma[1:3]) + 1:arma[4]] if (any(Mod(polyroot(c(1, ma))) < 1)) warning("seasonal MA part of model is not invertible") } z <- KalmanForecast(n.ahead, object$model) pred <- ts(z[[1]] + xm, start = xtsp[2] + deltat(rsd), frequency = xtsp[3]) if (se.fit) { se <- ts(sqrt(z[[2]] * object$sigma2), start = xtsp[2] + deltat(rsd), frequency = xtsp[3]) return(list(pred = pred, se = se)) } else re...

Hi, could anyone tell me how predict() predicts the new value(s), of a MA(1) arima-modell. its really easy to make it with an AR(1), knowing the last term, but how can i or R know the last error? It would also help if somebody could tell me how to find the "open" source of the function predict(). Thanks and sorry for my poor english. -- View this message in context:

...warning("MA part of model is not invertible") } if (arma[4L] > 0L) { ma <- coefs[sum(arma[1L:3L]) + 1L:arma[4L]] if (any(Mod(polyroot(c(1, ma))) < 1)) warning("seasonal MA part of model is not invertible") } z <- KalmanForecast(n.ahead, object$model) pred <- ts(z[[1L]] + xm, start = xtsp[2L] + deltat(rsd), frequency = xtsp[3L]) if (se.fit) { se <- ts(sqrt(z[[2L]] * object$sigma2), start = xtsp[2L] + deltat(rsd), frequency = xtsp[3L]) return(list(pred = pred, se = se))...

...argument like grep() and regexpr(). o [g]sub(perl = TRUE) support \L and \U in the replacement. o iconv() has been moved from 'utils' to 'base'. o identify()'s default method has additional arguments 'atpen' and 'tolerance' (following S). o KalmanForecast() and KalmanLike() now have an optional argument fast=FALSE to prevent their arguments being modified. o Exact p-values are available in ks.test() for the one-sided and two-sided one-sample Kolmogorov-Smirnov tests. o labels() now has a method for "dist" objects (replacing tha...

...argument like grep() and regexpr(). o [g]sub(perl = TRUE) support \L and \U in the replacement. o iconv() has been moved from 'utils' to 'base'. o identify()'s default method has additional arguments 'atpen' and 'tolerance' (following S). o KalmanForecast() and KalmanLike() now have an optional argument fast=FALSE to prevent their arguments being modified. o Exact p-values are available in ks.test() for the one-sided and two-sided one-sample Kolmogorov-Smirnov tests. o labels() now has a method for "dist" objects (replacing tha...