gdpc: An R Package for Generalized Dynamic Principal Components
Dublin Core
Title
gdpc: An R Package for Generalized Dynamic Principal Components
Subject
dimensionality reduction, high-dimensional time series, R.
Description
gdpc is an R package for the computation of the generalized dynamic principal components proposed in Peña and Yohai (2016). In this paper, we briefly introduce the problem
of dynamical principal components, propose a solution based on a reconstruction criteria
and present an automatic procedure to compute the optimal reconstruction. This solution
can be applied to the non-stationary case, where the components need not be a linear
combination of the observations, as is the case in the proposal of Brillinger (1981). This
article discusses some new features that are included in the package and that were not
considered in Peña and Yohai (2016). The most important one is an automatic procedure
for the identification of both the number of lags to be used in the generalized dynamic
principal components as well as the number of components required for a given reconstruction accuracy. These tools make it easy to use the proposed procedure in large data
sets. The procedure can also be used when the number of series is larger than the number
of observations. We describe an iterative algorithm and present an example of the use of
the package with real data
of dynamical principal components, propose a solution based on a reconstruction criteria
and present an automatic procedure to compute the optimal reconstruction. This solution
can be applied to the non-stationary case, where the components need not be a linear
combination of the observations, as is the case in the proposal of Brillinger (1981). This
article discusses some new features that are included in the package and that were not
considered in Peña and Yohai (2016). The most important one is an automatic procedure
for the identification of both the number of lags to be used in the generalized dynamic
principal components as well as the number of components required for a given reconstruction accuracy. These tools make it easy to use the proposed procedure in large data
sets. The procedure can also be used when the number of series is larger than the number
of observations. We describe an iterative algorithm and present an example of the use of
the package with real data
Creator
Daniel Peña
Source
https://www.jstatsoft.org/article/view/v092c02
Publisher
Universidad Carlos III
de Madrid
de Madrid
Date
February 2020
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Daniel Peña, “gdpc: An R Package for Generalized Dynamic Principal Components,” Repository Horizon University Indonesia, accessed March 14, 2025, https://repository.horizon.ac.id/items/show/8118.