tsoutliers: Identify and replace outliers in a time series

In forecast: Forecasting Functions for Time Series and Linear Models

Identify and replace outliers in a time series

Description

Uses supsmu for non-seasonal series and a periodic stl decomposition with seasonal series to identify outliers and estimate their replacements.

Usage

tsoutliers(x, iterate = 2, lambda = NULL)

Arguments

x	time series
iterate	the number of iterations required
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.

Value

index	Indicating the index of outlier(s)
replacement	Suggested numeric values to replace identified outliers

Author(s)

Rob J Hyndman

References

Hyndman (2021) "Detecting time series outliers" https://robjhyndman.com/hyndsight/tsoutliers/.

See Also

na.interp, tsclean

Examples

data(gold)
tsoutliers(gold)

forecast documentation built on Aug. 31, 2023, 5:13 p.m.