The R Journal: article published in 2017, volume 9:1

imputeTS: Time Series Missing Value Imputation in R
Steffen Moritz and Thomas Bartz-Beielstein , The R Journal (2017) 9:1, pages 207-218.

Abstract 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.

Received: 2016-07-12; online 2017-05-10
CRAN packages: AMELIA, mice, VIM, missMDA, imputeTS, zoo, forecast, spacetime, timeSeries, xts
CRAN Task Views implied by cited CRAN packages: TimeSeries, Finance, Econometrics, OfficialStatistics, Environmetrics, Multivariate, SocialSciences, SpatioTemporal, Psychometrics, Spatial

This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2017-009,
  author = {Steffen Moritz and Thomas Bartz-Beielstein},
  title = {{imputeTS: Time Series Missing Value Imputation in R}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-009},
  url = {https://doi.org/10.32614/RJ-2017-009},
  pages = {207--218},
  volume = {9},
  number = {1}
}