kssa (version 0.0.1)

get_imputations: get_imputations function

Description

Function to get imputations from methods compared by kssa

Usage

get_imputations(x_ts, methods = "all", seed = 1234)

Arguments

x_ts

A ts object with missing data to be imputed

methods

A string or string vector indicating the method or methods You can choose between the following:

• "all" - get imputed values for all methods - 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

seed

Numeric. Any number

Value

A list of imputed time series with the selected methods

Examples

# NOT RUN {
# Example 1: Get imputed values for airgap_na_ts with the methods of

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

# 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)

my_imputations <- get_imputations(airgap_na_ts, methods = "all")

# my_imputations contains the imputed time series with all methods.
# Access it and choose the one from the best method for your purposes

my_imputations$seadec
plot.ts(my_imputations$seadec)
# }
# NOT RUN {


# Example 2: Get imputed values for airgap_na_ts using only a subset of algorithms

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

# 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)

my_imputations <- get_imputations(airgap_na_ts, methods = c("linear_i", "locf"))

# my_imputations contains the imputed time series with all applied
# methods (locf and linear interpolation).
# Access it and choose the one from the best method for your purposes

my_imputations$locf
plot.ts(my_imputations$locf)


# }