imputeTS: Time Series Missing Value Imputation in R

By Steffen Moritz, Thomas Bartz-Beielstein. The R Journal, 2017

794 citations recorded by the source corpus.

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. that provides a collection of and tools this task. Using example time series, we illustrated the ease of use and the advantages of the provided functions. Simple algorithms as well as more complicated ones can be applied in the same simple and user-friendly manner.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1cf321816e71d3cbf7a3b576812fced0277fdfb8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, methodology): “Traditional time series imputation approaches have relied on statistical methodologies, such as mean imputation, spline interpolation [171], and regression models [172].”
BRITS: Bidirectional Recurrent Imputation for Time Series (2018, methodology): “ImputeTS is a widely used package for missing value imputation, which utilizes the state space model and kalman smoothing [23].”
Large Language Models Are Zero-Shot Time Series Forecasters (2023, methodology): “Simple imputation methods, such as nearest neighbor, are still core pre-processing steps in common data science workﬂows [32], and the choice of imputation method is especially relevant to clinical data, which often contains irregularly sampled measurements and where missingness can be meaningful…”