Analysis in R: Maybe not just interpolating missing values in time series? The “imputeTS” package

This is the “imputeTS” package for interpolating missing values in time series. It is easier to use R commands to manipulate missing values, but you may want to try this package first.

Package version is 3.3. Checked with R version 4.2.2.

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Install Package

Run the following command.

#Install Package
install.packages("imputeTS")

Example

See the command and package help for details.

#Loading the library
library("imputeTS")

#Create Data
#Converting to time series data with the ts command
TestData <- ts(c(2,3,4,5,6,NA,7,8))
TestData
Time Series:
Start = 1 
End = 8 
Frequency = 1 
[1]  2  3  4  5  6 NA  7  8

#Interpolate missing values in linear or in splines: na_interpolation command
#Specified by option
na_interpolation(TestData, option = "linear")
Time Series:
Start = 1 
End = 8 
Frequency = 1 
[1] 2.0 3.0 4.0 5.0 6.0 6.5 7.0 8.0

#Interpolate missing values by the value
#before or after the missing value: na_locf command
#locf: interpolate by previous value,nocb: interpolate by backward value
na_locf(TestData, option = "locf")
Time Series:
Start = 1 
End = 8 
Frequency = 1 
[1] 2 3 4 5 6 6 7 8

#Interpolate missing values by mean, median, mode: na_mean command
#option:mean,median,mode
na_mean(TestData, option = "mean")
Time Series:
Start = 1 
End = 8 
Frequency = 1 
[1] 2 3 4 5 6 5 7 8

#Randomly interpolate missing values: na_random command
#upper_bound: lower bound, upper_bound: upper bound
na_random(TestData, lower_bound = 100, upper_bound = 1000)
Time Series:
Start = 1 
End = 8 
Frequency = 1 
[1]   2.0000   3.0000   4.0000   5.0000   6.0000 911.4419   7.0000   8.0000

#Remove NA: na_remove command
na_remove(TestData)
Time Series:
Start = 1 
End = 7 
Frequency = 1 
[1] 2 3 4 5 6 7 8

#Replace NA: na_replace command
na_replace(TestData, fill = 99999)
Time Series:
Start = 1 
End = 8 
Frequency = 1 
[1]  2     3     4     5     6  99999  7  8

I hope this makes your analysis a little easier !!

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