# na.seasplit: Seasonally Splitted Missing Value Imputation In imputeTS: Time Series 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

 `1` ```na.seasplit(x, algorithm = "interpolation", ...) ```

## 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 "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 `...` 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

`na.interpolation`, `na.kalman`, `na.locf`, `na.ma`, `na.mean`, `na.random`, `na.replace`, `na.seadec`
 ```1 2 3 4 5 6 7 8``` ```#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") ```