R: Imputation Methods for Multivariate Locally Stationary Time Series

# Imputation Methods for Multivariate Locally Stationary Time Series

## Documentation for package ‘mvLSWimpute’ version 0.1.1

- DESCRIPTION file

## Help Pages

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