ImputeGAP Documentation¶

ImputeGAP is a comprehensive Python library for imputation of missing values in time series data. It implements user-friendly APIs to easily visualize, analyze, and repair incomplete time series datasets. The library supports a diverse range of imputation algorithms and modular missing data simulation catering to datasets with varying characteristics. ImputeGAP includes extensive customization options, such as automated hyperparameter tuning, benchmarking, explainability, and downstream evaluation.

In detail, the library provides:

• Over 40 state-of-the-art time series imputation algorithms from six different families

• Several imputation univariate time series datasets and utilities to handle multivariate ones

• Configurable contamination module that simulates real-world missingness patterns

• AutoML techniques to parameterize the imputation algorithms

• Unified benchmarking pipeline to evaluate the performance of imputation algorithms

• Modular analysis tools to assess the impact of imputation on time series downstream tasks

• Expainability module to understand the impact of time series features on the imputation results

• Adjustable wrappers to integrate new algorithms in different languages: Python, C++, Matlab, Java, and R

@article{nater2025imputegap,
  title = {ImputeGAP: A Comprehensive Library for Time Series Imputation},
  author = {Nater, Quentin and Khayati, Mourad and Pasquier, Jacques},
  year = {2025},
  eprint = {2503.15250},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
  url = {https://arxiv.org/abs/2503.15250}


@article{nater2025kdd,
  title = {A Hands-on Tutorial on Time Series Imputation with ImputeGAP},
  author = {Nater, Quentin and Khayati, Mourad and Cudré-Mauroux, Philippe},
  year = {2025},
  booktitle = {SIGKDD Conference on Knowledge Discovery and Data Mining (To Appear)},
  series = {KDD2025}
}