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, r-oldrel (x86_64): odpc_2.0.5.tgz |
| Old sources: | odpc archive |
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