Description Usage Arguments Value Author(s) References See Also Examples
Add a seasonal model to a state specification.
The seasonal model can be thought of as a regression on
nseasons
dummy variables with coefficients constrained to sum
to 1 (in expectation). If there are S
seasons then the state
vector gamma is of dimension S1
. The first
element of the state vector obeys
gamma[t+1, 1] = 1 * sum(gamma[t, 1]) + rnorm(1, 0, sigma)
1 2 3 4 5 6 7 8  AddSeasonal(
state.specification,
y,
nseasons,
season.duration = 1,
sigma.prior,
initial.state.prior,
sdy)

state.specification 
A list of state components that you wish to add to. If omitted, an empty list will be assumed. 
y 
The time series to be modeled, as a numeric vector. 
nseasons 
The number of seasons to be modeled. 
season.duration 
The number of time periods in each season. 
sigma.prior 
An object created by 
initial.state.prior 
An object created using

sdy 
The standard deviation of the series to be modeled. This
will be ignored if 
Returns a list with the elements necessary to specify a seasonal state model.
Steven L. Scott steve.the.bayesian@gmail.com
Harvey (1990), "Forecasting, structural time series, and the Kalman filter", Cambridge University Press.
Durbin and Koopman (2001), "Time series analysis by state space methods", Oxford University Press.
1 2 3 4 5 6 7  data(AirPassengers)
y < log(AirPassengers)
ss < AddLocalLinearTrend(list(), y)
ss < AddSeasonal(ss, y, nseasons = 12)
model < bsts(y, state.specification = ss, niter = 500)
pred < predict(model, horizon = 12, burn = 100)
plot(pred)

Loading required package: BoomSpikeSlab
Loading required package: Boom
Loading required package: MASS
Attaching package: 'Boom'
The following object is masked from 'package:stats':
rWishart
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: xts
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