ImputeGAP: A Comprehensive Library for Time Series Imputation

With the prevalence of sensor failures, imputation--the process of estimating missing values--has emerged as the cornerstone of time series data preparation. While numerous imputation algorithms have been developed to address these data gaps, existing libraries provide limited support. Furthermore, they often lack the ability to simulate realistic patterns of time series missing data and fail to account for the impact of imputation on subsequent downstream analysis. This paper introduces ImputeGAP, a comprehensive library for time series imputation that supports a diverse range of imputation methods and modular missing data simulation catering to datasets with varying characteristics. The library includes extensive customization options, such as automated hyperparameter tuning, benchmarking, explainability, downstream evaluation, and compatibility with popular time series frameworks.