ImputeGAP: A Comprehensive Library for Time Series Imputation
This addresses the need for better data preparation tools for researchers and practitioners dealing with sensor failures in time series data, though it appears incremental as it builds on existing imputation methods.
The paper tackles the problem of limited support and realistic missing data simulation in time series imputation libraries by introducing ImputeGAP, a comprehensive library that supports diverse imputation methods and modular simulation, but no concrete performance numbers are provided.
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.