MDPCA: Moving Dynamic Principal Component Analysis for Non-Stationary Multivariate Time Series

Fayed Awdah M Alshammri

MDPCA: Moving Dynamic Principal Component Analysis for Non-Stationary Multivariate Time Series

Fayed Awdah M Alshammri

Mathematics And Statistics

Research output: Other contribution

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Abstract

This function reduce the dimension of non-stationary (and stationary) multivariate time series by performing eigenanalysis on the moving cross-covriance matrix of the extended data matrix up to some specified lag. Notice that thefollowing libraries are needed to be installed before using the MDPCA function: library(roll); library(expm).

Original language	English
Type	Developing R Package
Media of output	R
Publisher	University of Strathclyde
Number of pages	1
Place of Publication	Glasgow
Publication status	Published - 10 Mar 2020

Keywords

MDPCA

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Alshammri-2020-MDPCA-Moving-Dynamic-Principal-Component-Analysis-for-Non-StationaryFinal published version, 94.1 KBLicence: GNU GPL 2

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title = "MDPCA: Moving Dynamic Principal Component Analysis for Non-Stationary Multivariate Time Series",

abstract = "This function reduce the dimension of non-stationary (and stationary) multivariate time series by performing eigenanalysis on the moving cross-covriance matrix of the extended data matrix up to some specified lag. Notice that thefollowing libraries are needed to be installed before using the MDPCA function: library(roll); library(expm).",

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author = "Alshammri, {Fayed Awdah M}",

note = "Alshammri, F. (2020). MDPCA: Moving Dynamic Principal Component Analysis for Non-Stationary Multivariate Time Series. R package version 0.1.0.",

year = "2020",

month = mar,

day = "10",

language = "English",

publisher = "University of Strathclyde",

type = "Other",

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PY - 2020/3/10

Y1 - 2020/3/10

N2 - This function reduce the dimension of non-stationary (and stationary) multivariate time series by performing eigenanalysis on the moving cross-covriance matrix of the extended data matrix up to some specified lag. Notice that thefollowing libraries are needed to be installed before using the MDPCA function: library(roll); library(expm).

AB - This function reduce the dimension of non-stationary (and stationary) multivariate time series by performing eigenanalysis on the moving cross-covriance matrix of the extended data matrix up to some specified lag. Notice that thefollowing libraries are needed to be installed before using the MDPCA function: library(roll); library(expm).

KW - MDPCA

M3 - Other contribution

PB - University of Strathclyde

CY - Glasgow

ER -

MDPCA: Moving Dynamic Principal Component Analysis for Non-Stationary Multivariate Time Series

Abstract

Keywords

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Cite this