acf: ACF/PACF Functions

In fChange: Functional Change Point Detection and Analysis

ACF/PACF Functions

Description

This function computes the ACF/PACF of data. This can be applied on traditional scalar time series or functional time series defined in dfts().

Usage

acf(x, lag.max = NULL, ...)

## Default S3 method:
acf(x, lag.max = NULL, ...)

pacf(x, lag.max = NULL, ...)

## Default S3 method:
pacf(x, lag.max = NULL, ...)

## S3 method for class 'dfts'
acf(
  x,
  lag.max = NULL,
  alpha = 0.05,
  method = c("Welch", "MC", "Imhof"),
  WWN = TRUE,
  figure = TRUE,
  ...
)

## S3 method for class 'dfts'
pacf(x, lag.max = NULL, n_pcs = NULL, alpha = 0.95, figure = TRUE, ...)

Arguments

x	Object for computation of (partial) autocorrelation function (see acf() or pacf).
lag.max	Number of lagged covariance estimators for the time series that will be used to estimate the (partial) autocorrelation function.
...	Additional parameters to appropriate function
alpha	A value between 0 and 1 that indicates significant level for the confidence interval for the i.i.d. bounds of the (partial) autocorrelation function. By default alpha = 0.05.
method	Character specifying the method to be used when estimating the distribution under the hypothesis of functional white noise. Accepted values are: "Welch": Welch approximation. "MC": Monte-Carlo estimation. "Imhof": Estimation using Imhof's method. By default, method = "Welch".
WWN	Logical. If TRUE, WWN bounds are also computed.
figure	Logical. If TRUE, prints plot for the estimated function with the specified bounds.
n_pcs	Number of principal components that will be used to fit the ARH(p) models.

Value

List with ACF or PACF values and plots

• acfs/pacfs: Autocorrelation values for each lag of the functional time series.

• SWN_bound: The upper prediction bound for the i.i.d. distribution under strong white noise assumption.

• WWN_bound: The upper prediction bound for the i.i.d. distribution under weak white noise assumption.

• plot: Plot of autocorrelation values for each lag of the functional time series.

References

Mestre G., Portela J., Rice G., Munoz San Roque A., Alonso E. (2021). Functional time series model identification and diagnosis by means of auto- and partial autocorrelation analysis. Computational Statistics & Data Analysis, 155, 107108.

Mestre, G., Portela, J., Munoz San Roque, A., Alonso, E. (2020). Forecasting hourly supply curves in the Italian Day-Ahead electricity market with a double-seasonal SARMAHX model. International Journal of Electrical Power & Energy Systems, 121, 106083.

Kokoszka, P., Rice, G., Shang, H.L. (2017). Inference for the autocovariance of a functional time series under conditional heteroscedasticity Journal of Multivariate Analysis, 162, 32–50.

See Also

stats::acf(), sacf()

Examples

acf(1:10)
x <- generate_brownian_bridge(100, seq(0, 1, length.out = 20))
acf(x, 20)

x <- generate_brownian_bridge(100, seq(0, 1, length.out = 20))
pacf(x, lag.max = 10, n_pcs = 2)

fChange documentation built on June 21, 2025, 9:08 a.m.