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

• ###### Imputation

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, …

• ###### Plotting

To plot missing data statistics for a time series x, run the following command:

 ggplot_na_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”).

• ###### Printing

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

 statsNA(x)
• ###### Datasets

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
ggplot_na_distribution Visualize Distribution of Missing Values
ggplot_na_intervals Missing Values Summarized in Intervals
ggplot_na_gapsize Visualize Distribution of NA Gapsizes
ggplot_na_imputations Visualize Imputed Values

More detailed information about the plots can be found in the imputeTS reference manual.

### Datasets

There are three 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 non-complete 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 Bartz-Beielstein, Thomas. “imputeTS: Time Series Missing Value Imputation in R.” R Journal 9.1 (2017). doi: 10.32614/RJ-2017-009.

### 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

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