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 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:

• Access to commonly used datasets in the time series imputation field

• Configurable contamination module that simulates real-world missingness patterns

• Parameterizable state-of-the-art time series imputation algorithms

• Extensive benchmarking to compare the performance of imputation algorithms

• Modular tools to assess the impact of imputation on key downstream tasks

• Fine-grained analysis of the impact of time series features on imputation results

• Seamless integration of new algorithms in 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}
}