odpc: One-Sided Dynamic Principal Components

Functions to compute the one-sided dynamic principal components ('odpc') introduced in Peña, Smucler and Yohai (2019) <doi:10.1080/01621459.2018.1520117>. 'odpc' is a novel dimension reduction technique for multivariate time series, that is useful for forecasting. These dynamic principal components are defined as the linear combinations of the present and past values of the series that minimize the reconstruction mean squared error.

Version: 2.0.5
Depends: R (≥ 3.3.0)
Imports: methods, Rcpp (≥ 0.12.7), forecast, parallel, doParallel, foreach, MASS
LinkingTo: Rcpp, RcppArmadillo (≥ 0.7.500.0.0)
Suggests: testthat
Published: 2022-03-02
Author: Daniel Peña, Ezequiel Smucler, Victor Yohai
Maintainer: Ezequiel Smucler <ezequiels.90 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: NEWS
In views: TimeSeries
CRAN checks: odpc results


Reference manual: odpc.pdf


Package source: odpc_2.0.5.tar.gz
Windows binaries: r-devel: odpc_2.0.5.zip, r-release: odpc_2.0.5.zip, r-oldrel: odpc_2.0.5.zip
macOS binaries: r-release (arm64): odpc_2.0.5.tgz, r-oldrel (arm64): odpc_2.0.5.tgz, r-release (x86_64): odpc_2.0.5.tgz
Old sources: odpc archive


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