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fdaACF
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Download and install fdaACF package within the R console
Install from CRAN:
install.packages("fdaACF")
Install from Github:
library("remotes")
install_github("cran/fdaACF") Install by package version:
library("remotes")
install_version("fdaACF", "1.0.0") Attach the package and use:
library("fdaACF")
Maintained by
Guillermo Mestre Marcos
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-01-24
Latest Update: 2020-10-20
Description:
Quantify the serial correlation across lags of a given functional
time series using the autocorrelation function and a partial autocorrelation
function for functional time series proposed in
Mestre et al. (2021) .
The autocorrelation functions are based on the L2 norm of the lagged covariance
operators of the series. Functions are available for estimating the
distribution of the autocorrelation functions under the assumption
of strong functional white noise.
How to cite:
Guillermo Mestre Marcos (2020). fdaACF: Autocorrelation Function for Functional Time Series. R package version 1.0.0, https://cran.r-project.org/web/packages/fdaACF. Accessed 26 Jun. 2026.
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Complete documentation for fdaACF
Functions, R codes and Examples using
the fdaACF R package
Some associated functions: FTS_identification . elec_prices . estimate_iid_distr_Imhof . estimate_iid_distr_MC . fdaACF . fit_ARHp_FPCA . integral_operator . mat2fd . obtain_FACF . obtain_FPACF . obtain_autocorrelation . obtain_autocov_eigenvalues . obtain_autocovariance . obtain_suface_L2_norm . plot_FACF . plot_autocovariance . reconstruct_fd_from_PCA . simulate_iid_brownian_bridge . simulate_iid_brownian_motion .
Some associated R codes: elec_prices.R . estimate_distribution.R . fdaACF.R . functional_autocorrelation.R . misc.R . plotting_functions.R . simulate_iid_series.R . Full fdaACF package functions and examples
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