In the vignette for the R package dlm: link on page 12, the author runs the function dlmSmooth to smooth the data and the function returns an object which is assigned to gasSmooth. Now, gasSmooth$s is described in the package help as:

Time series (or matrix) of smoothed values of the state vectors. The series starts one time unit before the first observation.

They say:

Based on the fitted model, we can compute the smoothing estimates of the states. This can be used to obtain a decomposition of the data into a smooth trend plus a stochastic seasonal component, subject to measurement error.

and then run:

gasSmooth <- dlmSmooth(lGas, mod = dlmGas)
x <- cbind(lGas, dropFirst(gasSmooth$s[,c(1,3)]))
colnames(x) <- c("Gas", "Trend", "Seasonal")

The author in the vignette selects to plot columns 1 and 3 of this timeseries matrix, but how do they know that these states represent the smoothed trend and the seasonal trend?


Have a look this : http://gradientdescending.com/state-space-models-for-time-series-analysis-and-the-dlm-package/

it says "The main output here is s. Again there are 13 columns, the first 2 from the linear trend model and the last columns from the seasonal component."


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