forecast (version 3.08)

decompose: Classical Seasonal Decomposition by Moving Averages

Description

Decompose a time series into seasonal, trend and irregular components using moving averages. Deals with additive or multiplicative seasonal component.

Usage

decompose(x, type=c("additive", "multiplicative"), filter=NULL)

Arguments

x

A time series.

type

The type of seasonal component. Can be abbreviated.

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.

Value

• 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

Details

The additive model used is: $$Y_t=T_t + S_t + e_t$$ The multiplicative model used is: $$Y_t=T_t\,S_t\, e_t$$ The function first determines the trend component using a moving average (if filter is NULL, a symmetric window with equal weights is used), and removes it from the time series. Then, the seasonal figure is computed by averaging, for each time unit, over all periods. The seasonal figure is then centered. Finally, the error component is determined by removing trend and seasonal figure (recycled as needed) from the original time series.

References

M. Kendall and A. Stuart (1983) The Advanced Theory of Statistics, Vol.3, Griffin, 410--414.

See Also

decompose

Examples

m <- decompose(co2)
plot(m)