imputeTS: Time Series Missing Value Imputation

Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: 'Mean', 'LOCF', 'Interpolation', 'Moving Average', 'Seasonal Decomposition', 'Kalman Smoothing on Structural Time Series models', 'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) <doi:10.32614/RJ-2017-009>.

Version: 3.2
Depends: R (≥ 3.0.1)
Imports: stats, grDevices, ggplot2 (≥ 3.3.0), ggtext, stinepack, forecast, magrittr, Rcpp
LinkingTo: Rcpp
Suggests: testthat, R.rsp, knitr, zoo, timeSeries, tis, xts, tibble, tsibble, rmarkdown
Published: 2021-01-16
Author: Steffen Moritz ORCID iD [aut, cre, cph], Sebastian Gatscha [aut], Earo Wang ORCID iD [ctb]
Maintainer: Steffen Moritz <steffen.moritz10 at>
License: GPL-3
NeedsCompilation: yes
Citation: imputeTS citation info
Materials: README NEWS
In views: MissingData, TimeSeries
CRAN checks: imputeTS results


Reference manual: imputeTS.pdf
Vignettes: Cheat Sheet imputeTS
imputeTS: Time Series Missing Value Imputation in R
Gallery: Times Series Missing Data Visualizations
Package source: imputeTS_3.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: imputeTS_3.2.tgz, r-oldrel: imputeTS_3.2.tgz
Old sources: imputeTS archive

Reverse dependencies:

Reverse imports: autostsm, EventDetectR, gimme, hpiR, imputeTestbench, RiverLoad, SLBDD, specmine, TrendSLR
Reverse suggests: airGR, baytrends, epimdr, naniar


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