simts: auto_corr – R documentation

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auto_corr

Empirical ACF and PACF

Description

This function can estimate either the autocovariance / autocorrelation for univariate time series, or the partial autocovariance / autocorrelation for univariate time series.

Usage

auto_corr(x, lag.max = NULL, pacf = FALSE, type = "correlation",
  demean = TRUE, robust = FALSE)

Arguments

`x`	A `vector` or `ts` object (of length N > 1).
`lag.max`	An `integer` indicating the maximum lag up to which to compute the empirical ACF / PACF.
`pacf`	A `boolean` indicating whether to output the PACF. If it's `TRUE`, then the function will only estimate the empirical PACF. If it's `FALSE` (the default), then the function will only estimate the empirical ACF.
`type`	A `character` string giving the type of acf to be computed. Allowed values are "correlation" (the default) and "covariance".
`demean`	A `boolean` indicating whether the data should be detrended (`TRUE`) or not (`FALSE`). Defaults to `TRUE`.
`robust`	A `boolean` indicating whether a robust estimator should be used (`TRUE`) or not (`FALSE`). Defaults to `FALSE`. This only works when the function is estimating ACF.

Details

lagmax default is 10*log10(N/m) where N is the number of observations and m is the number of time series being compared. If lagmax supplied is greater than the number of observations N, then one less than the total will be taken (i.e. N - 1).

Value

An array of dimensions N x 1 x 1.

Author(s)

Yuming Zhang

Examples

m = auto_corr(datasets::AirPassengers)
m = auto_corr(datasets::AirPassengers, pacf = TRUE)

simts

Time Series Analysis Tools

v0.1.1

AGPL-3 | file LICENSE

Authors

Stéphane Guerrier [aut, cre, cph], James Balamuta [aut, cph], Roberto Molinari [aut, cph], Justin Lee [aut], Yuming Zhang [aut], Wenchao Yang [ctb], Nathanael Claussen [ctb], Yunxiang Zhang [ctb], Christian Gunning [cph], Romain Francois [cph], Ross Ihaka [cph], R Core Team [cph]

Initial release

2019-07-21