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3 months ago

imputeTS: Time Series Missing Value Imputation in R

{Thomas Bartz-Beielstein Steffen Moritz}

imputeTS: Time Series Missing Value Imputation in R

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.

Benchmarks

BenchmarkMethodologyMetrics
multivariate-time-series-imputation-onImputeTS
MAE (PM2.5): 19.58
multivariate-time-series-imputation-on-1ImputeTS
MAE (10% of data as GT): 0.390
multivariate-time-series-imputation-on-uciImputeTS
MAE (10% missing): 0.363

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imputeTS: Time Series Missing Value Imputation in R | Papers | HyperAI