Papers › imputeTS: Time Series Missing Value Imputation in R
imputeTS: Time Series Missing Value Imputation in R
Steffen Moritz, Thomas Bartz-Beielstein
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multivariate Time Series Imputation | Beijing Multi-Site Air-Quality Dataset | ImputeTS | MAE (PM2.5) | 19.58 | #5 of 6 | Archive leaderboard | report |
| Multivariate Time Series Imputation | PhysioNet Challenge 2012 | ImputeTS | MAE (10% of data as GT) | 0.390 | #3 of 9 | Archive leaderboard | report |
| Multivariate Time Series Imputation | UCI localization data | ImputeTS | MAE (10% missing) | 0.363 | #3 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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