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fdaACF (version 0.1.0)

Autocorrelation Function for Functional Time Series

Description

Quantify the serial correlation across lags of a given functional time series using an autocorrelation function for functional time series. The autocorrelation function is based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation function under the assumption of strong functional white noise.

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Version

Install

install.packages('fdaACF')

Monthly Downloads

208

Version

0.1.0

License

GPL (>= 2)

Issues

0

Pull Requests

0

Stars

8

Forks

0

Maintainer

Guillermo Mestre Marcos

Last Published

January 24th, 2020

Functions in fdaACF (0.1.0)

simulate_iid_brownian_motion

Simulate a FTS from a brownian motion process
obtain_autocorrelation

Estimate the autocorrelation function of the series
plot_autocovariance

Generate a 3D plot of the autocovariance surface of a given FTS
simulate_iid_brownian_bridge

Simulate a FTS from a brownian bridge process
obtain_autocovariance

Estimate the autocovariance function of the series
fdaACF

fdaACF: Autocorrelation function for Functional Time Series
obtain_suface_L2_norm

Obtain L2 norm of the autocovariance functions
obtain_FACF

Obtain the autocorrelation function for a given functional time series.
obtain_autocov_eigenvalues

Estimate eigenvalues of the autocovariance function
plot_FACF

Plot the autocorrelation function of a given FTS
estimate_iid_distr_MC

Estimate distribution of the fACF under the iid. hypothesis using MC method
elec_prices

Daily electricity price profiles from the Day-Ahead Spanish Electricity Market
estimate_iid_distr_Imhof

Estimate distribution of the fACF under the iid. hypothesis using Imhof's method