similar to: Kalman Filter

Hello everybody, I am writing a review paper about State Space models in R, and I would like to cover as many packages as I reasonably can. So far I am familiar with the following tools to deal with SS models: * StructTS, Kalman* (in stats) * packages dse[1-2] * package sspir * package dlm I would like to have some input from users who work with SS models: are there any other packages for SS

Hi all, I've just started working my way through Mike West and Jeff Harrison's _Bayesian Forecasting and Dynamic Models_, and I was wondering if there were any publically-available packages to handle dynamic linear models, as they describe. I found the "dynlm" package, but either I don't yet understand what's going on or that package uses a different sense of the phrase

I familarized myelf with kalmanlike and structts which are approaches for building and estimating ( and forecasting ) state space models ( or the equivalent arima models ). back in 2003, gavin simpson wrote an email describing the west and harrison apprach to estimate state space models and asked if anything was out there for using that approach. the goals of this approach are the same as kalman

Hi, I am studying Kalman Filter and it seems to be difficult for me to apply the filter on a simple ARMA. It is easy to construct the state-space model, for instance: dlmModARMA(ar=c(0.4,-0.2),ma=c(0.2,-0.1, sigma2=1) but applying the dlmFilter on it, it doesn't work... I don't know if my problem is clear but if anyone has already worked on Kalman filter, it could be great to advise me!

Dear R users, Just to clarify. I am not offering to pay someone to do my Dissertation. These 4-5 commands on Kalman Filter would be only a tiny part of my 10,000 words dissertation. A part that even after trying for a few days, I am still stuck on. I am offering ?50, just to say thanks. Regards -- View this message in context:

Dear All, Could anyone?give me a hand?to suggest few packages in R to running Kalman prediction and filtration ? Thanks Fir

Hi all! I'm sure this must have been asked many times before but here goes anyway. I'm looking for a kalman filter in R for ar(i)ma time series. I'm sure there must be one around but it does not seem to be in either ts or tseries packages? Any suggestions welcome. Thanks Gerard Keogh The information in this email, and any attachments transmitted with it, are confidential and are

I have built a Kalman Filter model for flu forecasting as shown below. Y - Target Variable X1 - Predictor1 X2 - Predictor2 While forecasting into the future, I will NOT have data for all three variables. So, I am predicting X1 and X2 using two Kalman filters. The code is below x1.model <- dlmModSeas(52) + dlmModPoly(1, dV=5, dW=10) x2.model <- dlmModSeas(52) + dlmModPoly(1, dV=10,

help.search("Kalman") says to look at help(KalmanLike, package=ts). Andy -----Original Message----- From: Mohamed A. Kerasha [mailto:mohamed at engr.uconn.edu] Sent: Tuesday, November 19, 2002 9:27 AM To: r-help at stat.math.ethz.ch Subject: [R] Kalman Filter Hi all, Does any one know if there is Kalman Filter code or library in R. Thanks, Mohamed.

Hola, Estoy intentando implementar el filtro de Kalman para un modelo de series de tiempo que estoy haciendo, me gustaría saber si alguien me puede colaborar ya que soy principiante en R. Muchas gracias! Cordialmente, JAVIER SANTIAGO PARRA RAMOS INGENIERO DE SISTEMAS [[alternative HTML version deleted]]

Hi, I'm trying to use the kalman filter to estimate the variable drift of a random walk, given that I have a vector of time series data. Anyone have any thoughts on how to do this in R? Thanks, Alex [[alternative HTML version deleted]]

Dear all, I am pleased to announce the CRAN release of a new package called 'KFAS' - Kalman filter and smoother. The package KFAS contains functions of multivariate Kalman filter, smoother, simulation smoother and forecasting. It uses univariate approach algorithm (aka sequential processing), which is faster than normal method, and it also allows mean square prediction error matrix Ft to

Dear all, I am pleased to announce the CRAN release of a new package called 'KFAS' - Kalman filter and smoother. The package KFAS contains functions of multivariate Kalman filter, smoother, simulation smoother and forecasting. It uses univariate approach algorithm (aka sequential processing), which is faster than normal method, and it also allows mean square prediction error matrix Ft to

I am looking for a Kalman filter that can handle a control input. I thought that l.SS was suitable however, I can't get it to work, and wonder if I am not using the right function. What I want is a Kalman filter that accepts exogenous inputs where the input is found using the algebraic Ricatti equation solution to a penalty function. If K is the gain matrix then the exogenous input

Hi all R gurus out there, Im a kind of newbie to kalman-filters after some research I have found that the dlm package is the easiest to start with. So be patient if some of my questions are too basic. I would like to set up a beta estimation between an asset and a market index using a kalman-filter. Much littarture says it gives superior estimates compared to OLS estimates. So I would like to

Dear R Users, I am new to state-space modeling. I am using SSPIR package for Kalman Filter. I have a data set containing one dependent variable and 7 independent variables with 250 data points. I want to use Kalman Filter for forecast the future values of the dependent variable using a multiple regression framework. I have used ssm function to produce the state space (SS)

I have a problem involving state space models with a multivariate observation equation. In other words: the kalman filtering routines as implemented in the package ts cannot be used since it treats the univariate case only. My question : does a multivariate kalman filtering procedure for R exist somewhere in the world? Where could I perhaps expect to find something like that? Many thanks M.

If you mean this paper by Fahrmeir: http://biomet.oxfordjournals.org/cgi/content/abstract/81/2/317 I would recommend BayesX: http://www.stat.uni-muenchen.de/~bayesx/. BayesX interfaces with R and estimates discrete (and continuous) time survival data with penalized regression methods. If you are looking for a bona fide Bayesian survival analysis method and do not wish to spend a lot of time

  Hello All,   I am working with dlm for the purpose of estimating and forecasting with a Kalman filter model. I have succesfully set up the model and started generating results. Of course, I need to somehow be sure that the results make sense. Without any apparent target to compare with, my natural selection is the results by odinary least square. The idea being that if I choose a diffuse prior,

Hello, I would like use Kalman filter for estimating parameters of a stochastic model. I have developed the state space model but I don’t know the correct way use Kalman filter for parameter estimation. Has anybody experience in work with Kalman filter in R. I don’t know the correct function. Maybe it is - KalmanLike; but what is the correct Input? - tsmooth? -