| decompose {stats} | R Documentation |
Decompose a time series into seasonal, trend and irregular components using moving averages. Deals with additive or multiplicative seasonal component.
decompose(x, type = c("additive", "multiplicative"), filter = NULL)
x |
A time series. |
type |
The type of seasonal component. |
filter |
A vector of filter coefficients in reverse time order (as for
AR or MA coefficients), used for filtering out the seasonal
component. If NULL, a moving average with symmetric window is
performed. |
The additive model used is:
Y[t] = T[t] + S[t] + e[t]
The multiplicative model used is:
Y[t] = T[t] * S[t] + e[t]
An object of class "decomposed.ts" with following components:
seasonal |
The seasonal component (i.e., the repeated seasonal figure) |
figure |
The estimated seasonal figure only |
trend |
The trend component |
random |
The remainder part |
type |
The value of type |
The function stl provides a much more sophisticated
decomposition.
David Meyer david.meyer@ci.tuwien.ac.at
data(co2) m <- decompose(co2) m$figure plot(m)