tsnarmimp function

Remove and Impute Missing Values in a Multivariate Time Series

This function imputes missing values in a stationary multivariate time series using various methods, and removes cases with too many missing values.

tsnarmimp(
  X,
  max.missing = 0.8,
  na.rm.method = c("LE", "all"),
  na.impute = c("median.ma.spline", "median.ma", "median", "rnorm"),
  ma.terms = 3L
)

Arguments

• X: a T x n numeric data matrix (incl. ts or xts objects) or data frame of stationary time series.

• max.missing: numeric. Proportion of series missing for a case to be considered missing.

• na.rm.method: character. Method to apply concerning missing cases selected through max.missing: "LE" only removes cases at the beginning or end of the sample, whereas "all" always removes missing cases.

• na.impute: character. Method to impute missing values for the PCA estimates used to initialize the EM algorithm. Note that data are standardized (scaled and centered) beforehand. Available options are:

  "median"	simple series-wise median imputation.
  "rnorm"	imputation with random numbers drawn from a standard normal distribution.
  "median.ma"	values are initially imputed with the median, but then a moving average is applied to smooth the estimates.
  "median.ma.spline"	"internal" missing values (not at the beginning or end of the sample) are imputed using a cubic spline, whereas missing values at the beginning and end are imputed with the median of the series and smoothed with a moving average.

• ma.terms: the order of the (2-sided) moving average applied in na.impute methods "median.ma" and "median.ma.spline".

Returns

The imputed matrix X_imp, with attributes: - "missing": a missingness matrix W matching the dimensions of X_imp.

• "rm.rows": and a vector of indices of rows (cases) with too many missing values that were removed.

Examples

library(xts)
str(tsnarmimp(BM14_M))