Autocorrelation Function for Functional Time Series
Estimate distribution of the fACF under the iid. hypothesis using Imho...
Estimate distribution of the fACF under the iid. hypothesis using MC m...
fdaACF: Autocorrelation function for Functional Time Series
Fit an ARH(p) to a given functional time series
Obtain the auto- and partial autocorrelation functions for a given FTS
Integral transformation of a curve using an integral operator
Obtain a fd object from a matrix
Estimate the autocorrelation function of the series
Estimate eigenvalues of the autocovariance function
Estimate the autocovariance function of the series
Obtain the autocorrelation function for a given functional time series...
Obtain the partial autocorrelation function for a given FTS.
Obtain L2 norm of the autocovariance functions
Generate a 3D plot of the autocovariance surface of a given FTS
Plot the autocorrelation function of a given FTS
Obtain the reconstructed curves after PCA
Simulate a FTS from a brownian bridge process
Simulate a FTS from a brownian motion process
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) <doi:10.1016/j.csda.2020.107108>. 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.