forecast (version 8.16)

na.interp: Interpolate missing values in a time series

Description

By default, uses linear interpolation for non-seasonal series. For seasonal series, a robust STL decomposition is first computed. Then a linear interpolation is applied to the seasonally adjusted data, and the seasonal component is added back.

Usage

na.interp(
  x,
  lambda = NULL,
  linear = (frequency(x) 

Arguments

x

time series

lambda

Box-Cox transformation parameter. If lambda="auto", then a transformation is automatically selected using BoxCox.lambda. The transformation is ignored if NULL. Otherwise, data transformed before model is estimated.

linear

Should a linear interpolation be used.

Value

Time series

Details

A more general and flexible approach is available using na.approx in the zoo package.

See Also

tsoutliers

Examples

# NOT RUN {
data(gold)
plot(na.interp(gold))

# }