add.seasonal {bsts} | R Documentation |
Seasonal State Component
Description
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} = -\sum_{i = 2}^S \gamma_{t, i} + \epsilon_t
\qquad \epsilon_t \sim \mathcal{N}(0, \sigma)
Usage
AddSeasonal(
state.specification,
y,
nseasons,
season.duration = 1,
sigma.prior,
initial.state.prior,
sdy)
Arguments
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 |
Value
Returns a list with the elements necessary to specify a seasonal state model.
Author(s)
Steven L. Scott steve.the.bayesian@gmail.com
References
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.
See Also
Examples
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)