na.interp: Interpolate missing values in a time series

In 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))

forecast documentation built on March 18, 2026, 9:07 a.m.