This is mainly a wrapper for the Seasonally Adjusted Missing Value using Linear Interpolation function,
`na.interp()`

, from the `forecast`

R package. The `ts_impute_vec()`

function includes arguments for applying
seasonality to numeric vector (non-`ts`

) via the `period`

argument.

## Arguments

- x
A numeric vector.

- period
A seasonal period to use during the transformation. If

`period = 1`

, linear interpolation is performed. If`period > 1`

, a robust STL decomposition is first performed and a linear interpolation is applied to the seasonally adjusted data.- lambda
A box cox transformation parameter. If set to

`"auto"`

, performs automated lambda selection.

## Details

**Imputation using Linear Interpolation**

Three circumstances cause strictly linear interpolation:

**Period is 1:**With`period = 1`

, a seasonality cannot be interpreted and therefore linear is used.**Number of Non-Missing Values is less than 2-Periods**: Insufficient values exist to detect seasonality.**Number of Total Values is less than 3-Periods**: Insufficient values exist to detect seasonality.

**Seasonal Imputation using Linear Interpolation**

For seasonal series with `period > 1`

, a robust Seasonal Trend Loess (STL) decomposition is first computed.
Then a linear interpolation is applied to the seasonally adjusted data, and
the seasonal component is added back.

**Box Cox Transformation**

In many circumstances, a Box Cox transformation can help. Especially if the series is multiplicative
meaning the variance grows exponentially. A Box Cox transformation can be automated by setting `lambda = "auto"`

or can be specified by setting `lambda = numeric value`

.

## See also

Box Cox Transformation:

`box_cox_vec()`

Lag Transformation:

`lag_vec()`

Differencing Transformation:

`diff_vec()`

Rolling Window Transformation:

`slidify_vec()`

Loess Smoothing Transformation:

`smooth_vec()`

Fourier Series:

`fourier_vec()`

Missing Value Imputation for Time Series:

`ts_impute_vec()`

## Examples

```
library(dplyr)
# --- VECTOR ----
values <- c(1,2,3, 4*2, 5,6,7, NA, 9,10,11, 12*2)
values
#> [1] 1 2 3 8 5 6 7 NA 9 10 11 24
# Linear interpolation
ts_impute_vec(values, period = 1, lambda = NULL)
#> [1] 1 2 3 8 5 6 7 8 9 10 11 24
# Seasonal Interpolation: set period = 4
ts_impute_vec(values, period = 4, lambda = NULL)
#> [1] 1 2 3 8 5 6 7 8 9 10 11 24
# Seasonal Interpolation with Box Cox Transformation (internal)
ts_impute_vec(values, period = 4, lambda = "auto")
#> [1] 1.000000 2.000000 3.000000 8.000000 5.000000 6.000000 7.000000
#> [8] 7.960572 9.000000 10.000000 11.000000 24.000000
```