add.seasonal | R Documentation |
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 S-1
. The first
element of the state vector obeys
gamma[t+1, 1] = -1 * sum(gamma[t, -1]) + rnorm(1, 0, sigma)
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.
bsts
.
SdPrior
NormalPrior
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)
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