R-CMD-check Lifecycle: experimental

This package implements the Known Sub-Sequence Algorithm <doi: 10.1016/j.aaf.2021.12.013> which helps to automatically compare, validate and identify the best methods for missing data imputation in time series. It compares the performance of 11 state-of-the-art’s imputation methods avaliable from multiple CRAN packages and delivers a best method suited for each particular time series.


You can install the development version of kssa like so:



You can run kssa like in the following example that plots the results obtained when applying kssa to the example time series tsAirgapComplete.

# Create 20% random missing data in tsAirgapComplete time series from imputeTS
airgap_na <- missMethods::delete_MCAR(as.data.frame(tsAirgapComplete), 0.2)

# Convert co2_na to time series object
airgap_na_ts <- ts(airgap_na, start = c(1959, 1), end = c(1997, 12), frequency = 12)

# Apply the kssa algorithm with 5 segments,
# 10 iterations, 20% of missing data, and
# compare among all available methods in the package.
# Remember that percentmd must match with
# the real percentage of missing data in the
# input co2_na_ts time series

results_kssa <- kssa(airgap_na_ts,
  start_methods = "all",
  actual_methods = "all",
  segments = 5,
  iterations = 10,
  percentmd = 0.2

kssa_plot(results_kssa, type = "complete", metric = "rmse")

Example final plot

Conclusion: Since kssa_plot is ordered from lower to higher error (left to right), method ‘linear_i’ is the best to impute missing data in airgap_na_ts. Notice that method ‘locf’ is the worst

To obtain imputations with the best method, or any method of preference use function get_imputations().


You can cite kssa the following:

Ivan-Felipe Benavides, Steffen Moritz, Brayan-David Aroca-Gonzalez, Jhoana Romero, Marlon Santacruz and John-Josephraj Selvaraj (2022). kssa: Known Sub-Sequence Algorithm. R package version 0.0.1. https://github.com/pipeben/kssa