fdaACF: Autocorrelation Function for Functional Time Series

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.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: CompQuadForm, pracma
Suggests: testthat, fields
Published: 2020-01-24
Author: Guillermo Mestre Marcos [aut, cre], José Portela González [aut], Antonio Muñoz San Roque [ctb], Estrella Alonso Pérez [ctb]
Maintainer: Guillermo Mestre Marcos <guillermo.mestre at comillas.edu>
BugReports: https://github.com/GMestreM/fdaACF/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/GMestreM/fdaACF
NeedsCompilation: no
In views: FunctionalData, TimeSeries
CRAN checks: fdaACF results


Reference manual: fdaACF.pdf
Package source: fdaACF_0.1.0.tar.gz
Windows binaries: r-prerelease: fdaACF_0.1.0.zip, r-release: fdaACF_0.1.0.zip, r-oldrel: fdaACF_0.1.0.zip
macOS binaries: r-prerelease: fdaACF_0.1.0.tgz, r-release: fdaACF_0.1.0.tgz, r-oldrel: fdaACF_0.1.0.tgz


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