ImputeGAP: A Comprehensive Library for Time Series Imputation

Understand

With the prevalence of sensor failures, imputation, the process of estimating missing values, has emerged as the cornerstone of time series data pre-processing.

• While numerous imputation algorithms have been developed to repair these data gaps, existing time series libraries provide limited imputation support.
• Furthermore, they often lack the ability to simulate realistic time series missingness patterns and fail to account for the impact of the imputed data 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.