season {TSSS} | R Documentation |
Seasonal adjustment by state space modeling.
season(y, trend.order = 1, seasonal.order = 1, ar.order = 0, trade = FALSE, period = 12, tau2.ini = NULL, filter = c(1, length(y)), predict = length(y), arcoef.ini = NULL, log = FALSE, minmax = c(-1.0e+30, 1.0e+30), plot = TRUE, ...)
y |
a univariate time series. | ||
trend.order |
trend order. | ||
seasonal.order |
seasonal order. | ||
ar.order |
AR order. | ||
trade |
logical; if | ||
period |
number of seasons in one period. If the tsp attribute of
| ||
tau2.ini |
initial estimate of variance of the system noise τ^2, not equal to 1. | ||
filter |
a numerical vector of the form | ||
predict |
the end position of prediction (>= | ||
arcoef.ini |
initial estimate of AR coefficients (for | ||
log |
logical. If | ||
minmax |
lower and upper limits of observations. | ||
plot |
logical. If | ||
... |
further arguments to be passed to |
An object of class "season"
, which is a list with the following
elements:
tau2 |
variance of the system noise. |
sigma2 |
variance of the observational noise. |
llkhood |
log-likelihood of the model. |
aic |
AIC of the model. |
trend |
trend component (for |
seasonal |
seasonal component (for |
arcoef |
AR coefficients (for |
ar |
AR component (for |
day.effect |
trading day effect (for |
noise |
noise component. |
cov |
covariance matrix of smoother. |
Kitagawa, G. (2010) Introduction to Time Series Modeling. Chapman & Hall/CRC.
# BLSALLFOOD data data(BLSALLFOOD) season(BLSALLFOOD, trend.order = 2, seasonal.order = 1, ar.order = 2) season(BLSALLFOOD, trend.order = 2, seasonal.order = 1, ar.order = 2, filter = c(1, 132)) # Wholesale hardware data data(WHARD) season(WHARD, trend.order = 2, seasonal.order = 1, ar.order = 0, trade = TRUE, log = TRUE) season(WHARD, trend.order = 2, seasonal.order = 1, ar.order = 0, trade = TRUE, filter = c(1, 132), log = TRUE)