imputeTS: Time Series Missing Value Imputation in R

The imputeTS package specializes on univariate time series imputation. It offers multiple state-of-the-art imputation algorithm implementations along with plotting functions for time series missing data statistics. While imputation in general is a well-known problem and widely covered by R packages, finding packages able to fill missing values in univariate time series is more complicated. The reason for this lies in the fact, that most imputation algorithms rely on inter-attribute correlations, while univariate time series imputation instead needs to employ time dependencies. This paper provides an introduction to the imputeTS package and its provided algorithms and tools. Furthermore, it gives a short overview about univariate time series imputation in R. Introduction In almost every domain from industry (Billinton et al., 1996) to biology (Bar-Joseph et al., 2003), finance (Taylor, 2007) up to social science (Gottman, 1981) different time series data are measured. While the recorded datasets itself may be different, one common problem are missing values. Many analysis methods require missing values to be replaced with reasonable values up-front. In statistics this process of replacing missing values is called imputation. Time series imputation thereby is a special sub-field in the imputation research area. Most popular techniques like Multiple Imputation (Rubin, 1987), Expectation-Maximization (Dempster et al., 1977), Nearest Neighbor (Vacek and Ashikaga, 1980) and Hot Deck (Ford, 1983) rely on interattribute correlations to estimate values for the missing data. Since univariate time series do not possess more than one attribute, these algorithms cannot be applied directly. Effective univariate time series imputation algorithms instead need to employ the inter-time correlations. On CRAN there are several packages solving the problem of imputation of multivariate data. Most popular and mature (among others) are AMELIA (Honaker et al., 2011), mice (van Buuren and Groothuis-Oudshoorn, 2011), VIM (Kowarik and Templ, 2016) and missMDA (Josse and Husson, 2016). However, since these packages are designed for multivariate data imputation only they do not work for univariate time series. At the moment imputeTS (Moritz, 2016a) is the only package on CRAN that is solely dedicated to univariate time series imputation and includes multiple algorithms. Nevertheless, there are some other packages that include imputation functions as addition to their core package functionality. Most noteworthy being zoo (Zeileis and Grothendieck, 2005) and forecast (Hyndman, 2016). Both packages offer also some advanced time series imputation functions. The packages spacetime (Pebesma, 2012), timeSeries (Rmetrics Core Team et al., 2015) and xts (Ryan and Ulrich, 2014) should also be mentioned, since they contain some very simple but quick time series imputation methods. For a broader overview about available time series imputation packages in R see also (Moritz et al., 2015). In this technical report we evaluate the performance of several univariate imputation functions in R on different time series. This paper is structured as follows: Section Overview imputeTS package gives an overview, about all features and functions included in the imputeTS package. This is followed by Usage examples of the different provided functions. The paper ends with a Conclusions section. Overview imputeTS package The imputeTS package can be found on CRAN and is an easy to use package that offers several utilities for ’univariate, equi-spaced, numeric time series’. Univariate means there is just one attribute that is observed over time. Which leads to a sequence of single observations o1, o2, o3, ... on at successive points t1, t2, t3, ... tn in time. Equi-spaced means, that time increments between successive data points are equal |t1 − t2| = |t2 − t3| = ... = |tn−1 − tn|. Numeric means that the observations are measurable quantities that can be described as a number. In the first part of this section, a general overview about all available functions and datasets is given. The R Journal Vol. XX/YY, AAAA 20ZZ ISSN 2073-4859 Contributed research article 2 This is followed by more detailed overviews about the three areas covered by the package: ’Plots & Statistics’, ’Imputation’ and ’Datasets’. Information about how to apply these functions and tools can be found later in the Usage examples section. General overview As can be seen in Table 1, beyond several imputation algorithm implementations the package also includes plotting functions and datasets. The imputation algorithms can be divided into rather simple but fast approaches like mean imputation and more advanced algorithms that need more computation time like kalman smoothing on a structural model. Simple Imputation Imputation Plots & Statistics Datasets na.locf na.interpolation plotNA.distribution tsAirgap na.mean na.kalman plotNA.distributionBar tsAirgapComplete na.random na.ma plotNA.gapsize tsHeating na.replace na.seadec plotNA.imputations tsHeatingComplete na.remove na.seasplit statsNA tsNH4 tsNH4Complete Table 1: General Overview imputeTS package As a whole, the package aims to support the user in the complete process of replacing missing values in time series. This process starts with analyzing the distribution of the missing values using the statsNA function and the plots of plotNA.distribution, plotNA.distributionBar, plotNA.gapsize. In the next step the actual imputation can take place with one of the several algorithm options. Finally, the imputation results can be visualized with the plotNA.imputations function. Additionally, the package contains three datasets, each in a version with and without missing values, that can be used to test imputation algorithms. Plots & Statistics functions An overview about the available plots and statistics functions can be found in Table 2. To get a good impression what the plots look like section Usage examples is recommended. Function Description plotNA.distribution Visualize Distribution of Missing Values plotNA.distributionBar Visualize Distribution of Missing Values (Barplot) plotNA.gapsize Visualize Distribution of NA gap sizes plotNA.imputations Visualize Imputed Values statsNA Print Statistics about the Missing Data Table 2: Overview Plots & Statistics The statsNA function calculates several missing data statistics of the input data. This includes overall percentage of missing values, absolute amount of missing values, amount of missing value in different sections of the data, longest series of consecutive NAs and occurrence of consecutive NAs. The plotNA.distribution function visualizes the distribution of NAs in a time series. This is done using a standard time series plot, in which areas with missing data are colored red. This enables the user to see at first sight where in the series most of the missing values are located. The plotNA.distributionBar function provides the same insights to users, but is designed for very large time series. This is necessary for time series with 1000 and more observations, where it is not possible to plot each observation as a single point. The plotNA.gapsize function provides information about consecutive NAs by showing the most common NA gap sizes in the time series. The plotNA.imputations function is designated for visual inspection of the results after applying an imputation algorithm. Therefore, newly imputed observations are shown in a different color than the rest of the series. The R Journal Vol. XX/YY, AAAA 20ZZ ISSN 2073-4859 Contributed research article 3 Imputation functions An overview about all available imputation algorithms can be found in Table 3. Even if these functions are really easy applicable, some examples can be found later in section Usage examples. More detailed information about the theoretical background of the algorithms can be found in the imputeTS manual (Moritz, 2016b). Function Option Description na.interpolation linear Imputation by Linear Interpolation spline Imputation by Spline Interpolation stine Imputation by Stineman Interpolation na.kalman StructTS Imputation by Structural Model & Kalman Smoothing auto.arima Imputation by ARIMA State Space Representation & Kalman Sm. na.locf locf Imputation by Last Observation Carried Forward nocb Imputation by Next Observation Carried Backward na.ma simple Missing Value Imputation by Simple Moving Average linear Missing Value Imputation by Linear Weighted Moving Average exponential Missing Value Imputation by Exponential Weighted Moving Average na.mean mean MissingValue Imputation by Mean Value median Missing Value Imputation by Median Value mode Missing Value Imputation by Mode Value na.random Missing Value Imputation by Random Sample na.replace Replace Missing Values by a Defined Value na.seadec Seasonally Decomposed Missing Value Imputation na.seasplit Seasonally Splitted Missing Value Imputation na.remove Remove Missing Values Table 3: Overview Imputation Algorithms For convenience similar algorithms are available under one function name as parameter option. For example linear, spline and stineman interpolation are all included in the na.interpolation function. The na.mean, na.locf, na.replace, na.random functions are all simple and fast. In comparison, na.interpolation, na.kalman, na.ma, na.seasplit, na.seadec are more advanced algorithms that need more computation time. The na.remove function is a special case, since it only deletes all missing values. Thus, it is not really an imputation function. It should be handled with care since removing observations may corrupt the time information of the series. The na.seasplit and na.seadec functions are as well exceptions. These perform seasonal split / decomposition operations as a preprocessing step. For the imputation itself, one out of the other imputation algorithms can be used (which one can be set as option). Looking at all available imputation methods, no single overall best method can b