imputeTS: Time Series Missing Value Imputation
The imputeTS package specializes on (univariate) time series imputation. It offers several different imputation algorithm implementations. Beyond the imputation algorithms the package also provides plotting and printing functions of time series missing data statistics. Additionally three time series datasets for imputation experiments are included.
Installation
The imputeTS package can be found on CRAN. For installation execute in R:
install.packages("imputeTS")
If you want to install the latest version from GitHub (can be unstable) run:
library(devtools)
install_github("SteffenMoritz/imputeTS")
Usage

To impute (fill all missing values) in a time series x, run the following command:
na_interpolation(x)
Output is the time series x with all NA's replaced by reasonable values.
This is just one example for an imputation algorithm. In this case interpolation was the algorithm of choice for calculating the NA replacements. There are several other algorithms (see also under caption "Imputation Algorithms"). All imputation functions are named alike starting with na_ followed by a algorithm label e.g. na_mean, na_kalman, ...

To plot missing data statistics for a time series x, run the following command:
plotNA.distribution(x)
This is also just one example for a plot. Overall there are four different types of missing data plots. (see also under caption "Missing Data Plots").

To print statistics about the missing data in a time series x, run the following command:
statsNA(x)

To load the 'heating' time series (with missing values) into a variable y and the 'heating' time series (without missing values) into a variable z, run:
y < tsHeating z < tsHeatingComplete
There are three datasets provided with the package, the 'tsHeating', the 'tsAirgap' and the 'tsNH4' time series. (see also under caption "Datasets").
Imputation Algorithms
Here is a table with available algorithms to choose from:
Function  Description 

na_interpolation  Missing Value Imputation by Interpolation 
na_kalman  Missing Value Imputation by Kalman Smoothing 
na_locf  Missing Value Imputation by Last Observation Carried Forward 
na_ma  Missing Value Imputation by Weighted Moving Average 
na_mean  Missing Value Imputation by Mean Value 
na_random  Missing Value Imputation by Random Sample 
na_remove  Remove Missing Values 
na_replace  Replace Missing Values by a Defined Value 
na_seadec  Seasonally Decomposed Missing Value Imputation 
na_seasplit  Seasonally Splitted Missing Value Imputation 
This is a rather broad overview. The functions itself mostly offer more than just one algorithm. For example na_interpolation can be set to linear or spline interpolation.
More detailed information about the algorithms and their options can be found in the imputeTS reference manual.
Missing Data Plots
Here is a table with available plots to choose from:
Function  Description 

plotNA.distribution  Visualize Distribution of Missing Values 
plotNA.distributionBar  Visualize Distribution of Missing Values (Barplot) 
plotNA.gapsize  Visualize Distribution of NA gapsizes 
plotNA.imputations  Visualize Imputed Values 
More detailed information about the plots can be found in the imputeTS reference manual.
Datasets
There are two datasets (each in two versions) available:
Dataset  Description 

tsAirgap  Time series of monthly airline passengers (with NAs) 
tsAirgapComplete  Time series of monthly airline passengers (complete) 
tsHeating  Time series of a heating systems supply temperature (with NAs) 
tsHeatingComplete  Time series of a heating systems supply temperature (complete) 
tsNH4  Time series of NH4 concentration in a wastewater system (with NAs) 
tsNH4Complete  Time series of NH4 concentration in a wastewater system (complete) 
The tsAirgap, tsHeating and tsNH4 time series are with NAs. Their complete versions are without NAs. Except the missing values their versions are identical. The NAs for the time series were artifically inserted by simulating the missing data pattern observed in similar noncomplete time series from the same domain. Having a complete and incomplete version of the same dataset is useful for conducting experiments of imputation functions.
More detailed information about the datasets can be found in the imputeTS reference manual.
Reference
You can cite imputeTS the following:
Moritz, Steffen, and Thomas BartzBeielstein. "imputeTS: Time Series Missing Value Imputation in R." R Journal 9.1 (2017). doi: 10.32614/RJ2017009.
Need Help?
If you have general programming problems or need help using the package please ask your question on StackOverflow. By doing so all users will be able to benefit in the future from your question.
Don't forget to mark your question with the imputets tag on StackOverflow to get me notified
Support
If you found a bug or have suggestions, feel free to get in contact via steffen.moritz10 at gmail.com.
All feedback is welcome
Version
3.0
License
GPL3