kssa: kssa Algorithm

In kssa: Known Sub-Sequence Algorithm

kssa Algorithm

Description

Run the Known Sub-Sequence Algorithm to compare the performance of imputation methods on a time series of interest

Usage

kssa(
  x_ts,
  start_methods,
  actual_methods,
  segments = 5,
  iterations = 10,
  percentmd = 0.2,
  seed = 1234
)

Arguments

x_ts	Time series object ts containing missing data (NA)
start_methods	String vector. The method or methods to start the algorithm. Same as for actual_methods
actual_methods	The imputation methods to be compared and validated. It can be a string vector containing the following You can choose between the following: "all" - compare among all methods automatically - Default "auto.arima" - State space representation of an ARIMA model "StructTS" - State space representation of a structural model "seadec" - Seasonal decomposition with Kalman smoothing "linear_i" - Linear interpolation "spline_i" - Spline interpolation "stine_i" - Stineman interpolation "simple_ma" - Simple moving average "linear_ma" - Linear moving average "exponential_ma" - Exponential moving average "locf" - Last observation carried forward "stl" - Seasonal and trend decomposition with Loess For further details on these imputation methods please check packages imputeTS and forecast
segments	Integer. Into how many segments the time series will be divided
iterations	Integer. How many iterations to run
percentmd	Numeric. Percentage of missing data. Must match with the true percentage of missing data in x_ts
seed	Numeric. Random seed to choose

Value

A list of results to be plotted with function kssa_plot for easy interpretation

References

Benavides, I. F., Santacruz, M., Romero-Leiton, J. P., Barreto, C., & Selvaraj, J. J. (2022). Assessing methods for multiple imputation of systematic missing data in marine fisheries time series with a new validation algorithm. Aquaculture and Fisheries. Full text publication.

Examples

# Example 1: Compare all imputation methods

library("kssa")
library("imputeTS")

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

# Convert 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,
# compare among all available methods in the package.
# Remember that percentmd must match with
# the real percentage of missing data in the input time series

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

# Print and check results
results_kssa

# For an easy interpretation of kssa results
# please use function kssa_plot




# Example 2: Compare only locf and linear imputation

library("kssa")
library("imputeTS")

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

# Convert 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,
# compare among all applied methods (locf and linear interpolation).
# Remember that percentmd must match with
# the real percentage of missing data in the input time series

results_kssa <- kssa(airgap_na_ts,
  start_methods = c("locf", "linear_i"),
  actual_methods = c("locf", "linear_i"),
  segments = 5,
  iterations = 10,
  percentmd = 0.2
)

# Print and check results
results_kssa

# For an easy interpretation of kssa results
# please use function kssa_plot

kssa documentation built on June 22, 2022, 1:05 a.m.