my.acf: Autocovariance Functions via the Discrete Fourier Transform

In neuroconductor-devel/waveslim: Basic Wavelet Routines for One-, Two-, and Three-Dimensional Signal Processing

Description

Computes the autocovariance function (ACF) for a time series or the cross-covariance function (CCF) between two time series.

Usage

my.acf(x)
my.ccf(a, b)

Arguments

x,a,b

time series

Details

The series is zero padded to twice its length before the discrete Fourier transform is applied. Only the values corresponding to nonnegative lags are provided (for the ACF).

Value

The autocovariance function for all nonnegative lags or the cross-covariance function for all lags.

Author(s)

B. Whitcher

Examples

data(ibm)
ibm.returns <- diff(log(ibm))
plot(1:length(ibm.returns) - 1, my.acf(ibm.returns), type="h",
     xlab="lag", ylab="ACVS", main="Autocovariance Sequence for IBM Returns")

neuroconductor-devel/waveslim documentation built on May 3, 2021, 5:31 a.m.