similar to: Forecasting with dlm

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

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

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

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,

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],