Displaying 5 results from an estimated 5 matches similar to: "Forecasting with dlm"
2014 Jan 08
0
Strange behaviour of `dlm` package
Dear R-help!
I have encountered strange behaviour (that is, far-off filtering, smoothing
and forecast distributions under certain conditions) in the `dlm` package by
Giovanni Petris.
Here is an example:
I use the annual hotel bookings time series data, which I model using a
second order polinomial DLM.
First I perform the analysis with the data in logarithmic form and
everything seems to be
2011 Jul 23
2
How to solve ergodic density/distribution using R
May I have a question on how to solve the following problem by R code?
Mainly we want to solve the equation show in the attached image. The equation is a continuous version of Markov process.
In the equation, we have been able to achieve two things using R code:
[1] From year-2009 sample data, we can estimate the marginal density ?f(x ; 2009)? by using R function ?density()?
[2] From
2011 Jun 07
2
Setting up a State Space Model in dlm
This question pertains to setting up a model in the package "dlm"
(dynamic linear models,
http://cran.r-project.org/web/packages/dlm/index.html
I have read both the vignette and?"An R Package for Dynamic Linear
Models" (http://www.jstatsoft.org/v36/i12/paper), both of which are
very helpful. There is also some discussion at
2013 Mar 08
0
using dlmModPoly in library dlm
Hi Group,
I'm trying to build a model to predict a product's sale price. I'm
researching the dlm package. Looks like I should use dlmModPoly, dlmMLE,
dlmFilter, dlmSmooth, and finally dlmForecast. I'm looking at the Nile
River example and I have a few questions:
1.
If I only want to predict future sale price based on observed sale
price, I should use a univariate model,
2011 Feb 22
0
Problem with forward prediction using StructTS output
I am having problems with forward prediction using the output of the
Basic Structural Model from StructTS. The following snippet
illustrates the problem:
t_end <- 139
nahead <- 20
data(AirPassengers)
ap <- log10(AirPassengers)-2
fit <- StructTS(ts(ap[1:t_end], freq=12), type="BSM")
p <- stats:::predict.StructTS(fit, n.ahead=nahead)
plot(1:t_end, ap[1:t_end],