mvLSWimpute (version 0.1.1)

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

Description

This function applies the mvLSWimpute method to impute missing values in a multivariate locally stationary time series. The imputation can be based on forecasts only or use information from both a forecasting and backcasting step.

Usage

mv_impute(data, p = 2, type = "forward", index = NULL)

Value

Returns a list containing the following elements:

ImputedData

Matrix containing the imputed time series.

missing.index

Vector containing the set of time indices that have missing values.

Arguments

data

Input multivariate time series, matrix of dimension TxP where P is the number of channels and T is the length of the series.

p

The number of terms to include in the clipped predictor when carrying out one step ahead forecasting/backcasting.

type

The type of imputation to carry out, either "forward" or "forward-backward"

index

The set of time indices containing missing values, this is NULL by default and will be determined from the input series.

Author

Rebecca Wilson

References

Wilson, R. E., Eckley, I. A., Nunes, M. A. and Park, T. (2021) A wavelet-based approach for imputation in nonstationary multivariate time series. _Statistics and Computing_ *31* Article 18, doi:10.1007/s11222-021-09998-2.

Examples

set.seed(1)
X <- matrix(rnorm(2 * 2^8), ncol = 2)
X[1:2^7, 2] <- 3 * (X[1:2^7, 2] + 0.95 * X[1:2^7, 1])
X[-(1:2^7), 2] <- X[-(1:2^7), 2] - 0.95 * X[-(1:2^7), 1]
X[-(1:2^7), 1] <- X[-(1:2^7), 1] * 4
X <- as.ts(X)

# create some fake missing data, taking care not to have missingness hear the start of the series
missing.index = sort(sample(10:2^8, 30))

X[missing.index, ] <- NA

newdata = mv_impute(X)