na_seasplit {imputeTS}

Seasonally Splitted Missing Value Imputation

Description

Splits the times series into seasons and afterwards performs imputation separately for each of the resulting time series datasets (each containing the data for one specific season).

Usage

na_seasplit(
  x,
  algorithm = "interpolation",
  find_frequency = FALSE,
  maxgap = Inf,
  ...
)

Arguments

x

Numeric Vector (vector) or Time Series (ts) object in which missing values shall be replaced

algorithm

Algorithm to be used after splits. Accepts the following input:

• "interpolation" - Imputation by Interpolation (default choice)

• "locf" - Imputation by Last Observation Carried Forward

• "mean" - Imputation by Mean Value

• "random" - Imputation by Random Sample

• "kalman" - Imputation by Kalman Smoothing and State Space Models

• "ma" - Imputation by Weighted Moving Average

find_frequency

If TRUE the algorithm will try to estimate the frequency of the time-series automatically.

maxgap

Maximum number of successive NAs to still perform imputation on. Default setting is to replace all NAs without restrictions. With this option set, consecutive NAs runs, that are longer than 'maxgap' will be left NA. This option mostly makes sense if you want to treat long runs of NA afterwards separately.

...

Additional parameters for these algorithms that can be passed through. Look at na_interpolation, na_locf, na_random, na_mean for parameter options.

Value

Vector (vector) or Time Series (ts) object (dependent on given input at parameter x)

Author(s)

Steffen Moritz

See Also

na_interpolation, na_kalman, na_locf, na_ma, na_mean, na_random, na_replace, na_seadec

Examples

# Example 1: Perform seasonal splitted imputation using algorithm = "interpolation"
na_seasplit(tsAirgap, algorithm = "interpolation")

# Example 2: Perform seasonal splitted imputation using algorithm = "mean"
na_seasplit(tsAirgap, algorithm = "mean")

# Example 3: Same as example 1, just written with pipe operator
tsAirgap %>% na_seasplit(algorithm = "interpolation")