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mvLSWimpute (version 0.1.1)

Imputation Methods for Multivariate Locally Stationary Time Series

Description

Implementation of imputation techniques based on locally stationary wavelet time series forecasting methods from Wilson, R. E. et al. (2021) .

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Install

install.packages('mvLSWimpute')

Monthly Downloads

177

Version

0.1.1

License

GPL-2

Maintainer

Matt Nunes

Last Published

August 16th, 2022

Functions in mvLSWimpute (0.1.1)

mvLSWimpute-package

tools:::Rd_package_title("mvLSWimpute")
haarWT

Function to apply the (univariate) Haar wavelet transform
spec_estimation

Function to estimate the Local Wavelet Spectral matrix for a multivariate locally stationary time series containing missing values
pdef

Function to regularise the LWS matrix.
mv_impute

Function to apply the mvLSWimpute method and impute missing values in a multivariate locally stationary time series
correct_per

Function to smooth the raw wavelet periodogram
form_lacv_forward

Function to form the local autocovariance array for the forecasting / backcasting step.
smooth_per

Function to smooth the raw wavelet periodogram using the default mvLSW routine.
pred_eq_forward

Function to form the prediction equations for the forecasting / backcasting step.