forecast: na.interp – R documentation

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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) <= 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

Examples

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

forecast

Forecasting Functions for Time Series and Linear Models

v8.14

GPL-3

Authors

Rob Hyndman [aut, cre, cph] (<https://orcid.org/0000-0002-2140-5352>), George Athanasopoulos [aut], Christoph Bergmeir [aut] (<https://orcid.org/0000-0002-3665-9021>), Gabriel Caceres [aut], Leanne Chhay [aut], Mitchell O'Hara-Wild [aut] (<https://orcid.org/0000-0001-6729-7695>), Fotios Petropoulos [aut] (<https://orcid.org/0000-0003-3039-4955>), Slava Razbash [aut], Earo Wang [aut], Farah Yasmeen [aut] (<https://orcid.org/0000-0002-1479-5401>), R Core Team [ctb, cph], Ross Ihaka [ctb, cph], Daniel Reid [ctb], David Shaub [ctb], Yuan Tang [ctb] (<https://orcid.org/0000-0001-5243-233X>), Zhenyu Zhou [ctb]

Initial release