facf: Functional autocorrelation function

In ftsa: Functional Time Series Analysis

Functional autocorrelation function

Description

Compute functional autocorrelation function at various lags

Usage

facf(fun_data, lag_value_range = seq(0, 20, by = 1))

Arguments

fun_data	A data matrix of dimension (n by p), where n denotes sample size; and p denotes dimensionality
lag_value_range	Lag value

Details

The autocovariance at lag i is estimated by the function \widehat{\gamma}_i(t,s), a functional analog of the autocorrelation is defined as

\widehat{\rho}_i = \frac{\|\widehat{\gamma}_i\|}{\int \widehat{\gamma}_0(t,t)dt}.

Value

A vector of functional autocorrelation function at various lags

Author(s)

Han Lin Shang

References

L. Horv\'ath, G. Rice and S. Whipple (2016) Adaptive bandwidth selection in the long run covariance estimator of functional time series, Computational Statistics and Data Analysis, 100, 676-693.

Examples

facf_value = facf(fun_data = t(ElNino_ERSST_region_1and2$y))

ftsa documentation built on May 29, 2024, 2:47 a.m.