auto_clean: Automatic Cleaning of Outliers and Temporary Changes

In tsaux: Time Series Forecasting Auxiliary Functions

Automatic Cleaning of Outliers and Temporary Changes

Description

A wrapper function for tso from the tsoutliers package. Takes as input a univariate xts object and returns a series decontaminated from outliers and temporary changes.

Usage

auto_clean(
  y,
  frequency = 1,
  lambda = NULL,
  types = c("AO", "TC"),
  stlm_opts = list(etsmodel = "AAN"),
  auto_arima_opts = list(max.p = 1, max.q = 1, d = 1, allowdrift = FALSE),
  method = c("sequential", "full"),
  ...
)

Arguments

y	a univariate xts object.
frequency	the frequency of the time series. If the frequency is 1 then seasonal estimation will be turned off. Will also accept multiple seasonal frequencies.
lambda	an optional Box Cox transformation parameter. The routines are then run on the transformed dataset.
types	the types of anomalies to search and decontaminate series from. Defaults to Additive outliers and temporary changes. Can be enhanced with trend breaks but not suggested for the purpose of forecasting.
stlm_opts	additional arguments to the stlm function.
auto_arima_opts	additional arguments to the auto.arima function in the tso routine.
method	whether to apply a sequential identification of anomalies using STL decomposition in order to only pass the stationary residuals to the tso function, else to pass the series directly to the tso package.
...	any additional arguments passed to the tso functions (refer to the documentation of the tsoutliers package).

Details

Calls the auto_regressors function to obtain the matrix of regressors and coefficients which are then used to decontaminate the series. If lambda is not NULL, the series is first transformed to perform the decontamination and then back transformed afterwards.

Value

A xts vector.

Author(s)

Alexios Galanos for this wrapper function.
Rob Hyndman for the forecast package.
Javier López-de-Lacalle for the tsoutliers package.

tsaux documentation built on April 4, 2025, 3:08 a.m.