Description Usage Arguments Value Author(s) See Also Examples

View source: R/estimate_impute_AR1_Gaussian.R

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`

.

1 2 3 4 5 6 7 8 9 10 | ```
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
)
``` |

`y` |
Time series object coercible to either a numeric vector or numeric matrix
(e.g., |

`rolling_window` |
Rolling window length (default is |

`random_walk` |
Logical value indicating if the time series is assumed to be a random walk so that |

`zero_mean` |
Logical value indicating if the time series is assumed zero-mean so that |

`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 |

`tol` |
Positive number denoting the relative tolerance used as stopping criterion (default is |

`maxiter` |
Positive integer indicating the maximum number of iterations allowed (default is |

Same as `impute_AR1_Gaussian`

for the case `n_samples = 1`

and `return_estimates = FALSE`

.

Daniel P. Palomar

`plot_imputed`

, `impute_AR1_Gaussian`

1 2 3 4 5 | ```
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)
``` |

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