Learn R Programming

tsBSS (version 1.0.0)

Blind Source Separation and Supervised Dimension Reduction for Time Series

Description

Different estimators are provided to solve the blind source separation problem for multivariate time series with stochastic volatility and supervised dimension reduction problem for multivariate time series. Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace. The package is fully described in Nordhausen, Matilainen, Miettinen, Virta and Taskinen (2021) .

Copy Link

Version

Install

install.packages('tsBSS')

Monthly Downloads

271

Version

1.0.0

License

GPL (>= 2)

Maintainer

Markus Matilainen

Last Published

July 10th, 2021

Functions in tsBSS (1.0.0)

PVC

A Modified Algorithm for Principal Volatility Component Estimator
SOBIasymp

Second-order Separation Sub-White-Noise Asymptotic Testing with SOBI
summary.tssdr

Summary of an Object of Class tssdr
AMUSEladle

Ladle Estimator to Estimate the Number of White Noise Components in SOS with AMUSE
tsBSS-package

Blind Source Separation and Supervised Dimension Reduction for Time Series
FixNA

The FixNA Method for Blind Source Separation
gSOBI

Generalized SOBI
vSOBI

A Variant of SOBI for Blind Source Separation
tssdr

Supervised Dimension Reduction for Multivariate Time Series
lbtest

Modified Ljung-Box Test and Volatility Clustering Test for Time Series.
gJADE

Generalized JADE
AMUSEboot

Second-order Separation Sub-White-Noise Bootstrap Testing with AMUSE
gFOBI

Generalized FOBI
AMUSEasymp

Second-order Separation Sub-White-Noise Asymptotic Testing with AMUSE
bssvol

Class: bssvol
SOBIboot

Second-order Separation Sub-White-Noise Bootstrap Testing with SOBI
SOBIladle

Ladle Estimator to Estimate the Number of White Noise Components in SOS with SOBI
WeeklyReturnsData

Logarithmic Returns of Exchange Rates of 7 Currencies Against US Dollar