Imputation Methods for Multivariate Locally Stationary Time Series

Documentation for package ‘mvLSWimpute’ version 0.1.1

mvLSWimpute-package	Imputation Methods for Multivariate Locally Stationary Time Series
correct_per	Function to smooth the raw wavelet periodogram
form_lacv_backward	Function to form the local autocovariance array for the forecasting / backcasting step.
form_lacv_forward	Function to form the local autocovariance array for the forecasting / backcasting step.
haarWT	Function to apply the (univariate) Haar wavelet transform
mvLSWimpute	Imputation Methods for Multivariate Locally Stationary Time Series
mv_impute	Function to apply the mvLSWimpute method and impute missing values in a multivariate locally stationary time series
pdef	Function to regularise the LWS matrix.
pred_eq_backward	Function to form the prediction equations for the forecasting / backcasting step.
pred_eq_forward	Function to form the prediction equations for the forecasting / backcasting step.
smooth_per	Function to smooth the raw wavelet periodogram using the default 'mvLSW' routine.
spec_estimation	Function to estimate the Local Wavelet Spectral matrix for a multivariate locally stationary time series containing missing values