ts_impute_vec: Missing Value Imputation for Time Series

View source: R/vec-ts_impute.R

ts_impute_vecR Documentation

Missing Value Imputation for Time Series


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.


ts_impute_vec(x, period = 1, lambda = NULL)



A numeric vector.


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.


A box cox transformation parameter. If set to "auto", performs automated lambda selection.


Imputation using Linear Interpolation

Three circumstances cause strictly linear interpolation:

  1. Period is 1: With period = 1, a seasonality cannot be interpreted and therefore linear is used.

  2. Number of Non-Missing Values is less than 2-Periods: Insufficient values exist to detect seasonality.

  3. 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.


A numeric vector with the missing values imputed.


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()



# --- VECTOR ----

values <- c(1,2,3, 4*2, 5,6,7, NA, 9,10,11, 12*2)

# Linear interpolation
ts_impute_vec(values, period = 1, lambda = NULL)

# Seasonal Interpolation: set period = 4
ts_impute_vec(values, period = 4, lambda = NULL)

# Seasonal Interpolation with Box Cox Transformation (internal)
ts_impute_vec(values, period = 4, lambda = "auto")

timetk documentation built on Nov. 2, 2023, 6:18 p.m.