impute_rolling_AR1_Gaussian: Impute missing values of time series on a rolling window...

In imputeFin: Imputation of Financial Time Series with Missing Values and/or Outliers

Description

Impute inner missing values (excluding leading and trailing ones) of time series on a rolling window basis. This is a wrapper of the function impute_AR1_Gaussian.

Usage

impute_rolling_AR1_Gaussian(
  y,
  rolling_window = 252,
  random_walk = FALSE,
  zero_mean = FALSE,
  remove_outliers = FALSE,
  outlier_prob_th = 0.001,
  tol = 1e-10,
  maxiter = 100
)

Arguments

y	Time series object coercible to either a numeric vector or numeric matrix (e.g., zoo or xts) with missing values denoted by NA.
rolling_window	Rolling window length (default is 252).
random_walk	Logical value indicating if the time series is assumed to be a random walk so that phi1 = 1 (default is FALSE).
zero_mean	Logical value indicating if the time series is assumed zero-mean so that phi0 = 0 (default is FALSE).
remove_outliers	Logical value indicating whether to detect and remove outliers.
outlier_prob_th	Threshold of probability of observation to declare an outlier (default is 1e-3).
tol	Positive number denoting the relative tolerance used as stopping criterion (default is 1e-8).
maxiter	Positive integer indicating the maximum number of iterations allowed (default is 100).

Value

Same as impute_AR1_Gaussian for the case n_samples = 1 and return_estimates = FALSE.

Author(s)

Daniel P. Palomar

See Also

plot_imputed, impute_AR1_Gaussian

Examples

library(imputeFin)
data(ts_AR1_Gaussian) 
y_missing <- ts_AR1_Gaussian$y_missing
y_imputed <- impute_rolling_AR1_Gaussian(y_missing)
plot_imputed(y_imputed)

imputeFin documentation built on Feb. 20, 2021, 9:07 a.m.