na.interp: Interpolate missing values in a time series

In robjhyndman/forecast: Forecasting Functions for Time Series and Linear Models

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) <= 1 | sum(!is.na(x)) <= 2 * 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.

Details

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

Value

Time series

Author(s)

Rob J Hyndman

See Also

tsoutliers

Examples

data(gold)
plot(na.interp(gold))

robjhyndman/forecast documentation built on April 20, 2024, 4:52 a.m.