add.random.walk.holiday {bsts} | R Documentation |
Random Walk Holiday State Model
Description
Adds a random walk holiday state model to the state specification. This model says
%
y_t = \alpha_{d(t), t} + \epsilon_t
where there is one element in \alpha_t
for each day
in the holiday influence window. The transition equation is
%
\alpha_{d(t+1), t+1} = \alpha_{d(t+1), t} + \epsilon_{t+1}
if t+1 occurs on day d(t+1) of the influence window, and
\alpha_{d(t+1), t+1} = \alpha_{d(t+1), t} %
otherwise.
Usage
AddRandomWalkHoliday(state.specification = NULL,
y,
holiday,
time0 = NULL,
sigma.prior = NULL,
initial.state.prior = NULL,
sdy = sd(as.numeric(y), na.rm = TRUE))
Arguments
state.specification |
A list of state components that you wish augment. If omitted, an empty list will be assumed. |
y |
The time series to be modeled, as a numeric vector
convertible to |
holiday |
An object of class |
time0 |
An object convertible to |
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
A list describing the specification of the random walk holiday state model, formatted as expected by the underlying C++ code.
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
bsts
.
RegressionHolidayStateModel
HierarchicalRegressionHolidayStateModel
Examples
trend <- cumsum(rnorm(730, 0, .1))
dates <- seq.Date(from = as.Date("2014-01-01"), length = length(trend),
by = "day")
y <- zoo(trend + rnorm(length(trend), 0, .2), dates)
AddHolidayEffect <- function(y, dates, effect) {
## Adds a holiday effect to simulated data.
## Args:
## y: A zoo time series, with Dates for indices.
## dates: The dates of the holidays.
## effect: A vector of holiday effects of odd length. The central effect is
## the main holiday, with a symmetric influence window on either side.
## Returns:
## y, with the holiday effects added.
time <- dates - (length(effect) - 1) / 2
for (i in 1:length(effect)) {
y[time] <- y[time] + effect[i]
time <- time + 1
}
return(y)
}
## Define some holidays.
memorial.day <- NamedHoliday("MemorialDay")
memorial.day.effect <- c(.3, 3, .5)
memorial.day.dates <- as.Date(c("2014-05-26", "2015-05-25"))
y <- AddHolidayEffect(y, memorial.day.dates, memorial.day.effect)
presidents.day <- NamedHoliday("PresidentsDay")
presidents.day.effect <- c(.5, 2, .25)
presidents.day.dates <- as.Date(c("2014-02-17", "2015-02-16"))
y <- AddHolidayEffect(y, presidents.day.dates, presidents.day.effect)
labor.day <- NamedHoliday("LaborDay")
labor.day.effect <- c(1, 2, 1)
labor.day.dates <- as.Date(c("2014-09-01", "2015-09-07"))
y <- AddHolidayEffect(y, labor.day.dates, labor.day.effect)
## The holidays can be in any order.
holiday.list <- list(memorial.day, labor.day, presidents.day)
number.of.holidays <- length(holiday.list)
## In a real example you'd want more than 100 MCMC iterations.
niter <- 100
ss <- AddLocalLevel(list(), y)
ss <- AddRandomWalkHoliday(ss, y, memorial.day)
ss <- AddRandomWalkHoliday(ss, y, labor.day)
ss <- AddRandomWalkHoliday(ss, y, presidents.day)
model <- bsts(y, state.specification = ss, niter = niter, seed = 8675309)
## Plot model components.
plot(model, "comp")
## Plot the effect of the specific state component.
plot(ss[[2]], model)