Although missing gaps are common in time series data, most existing imputation libraries have a narrow focus. They typically rely on a limited set of techniques and make overly simplistic assumptions about the nature of missing data. Consequently, they fail to model the true intricate complexity of real-world time series. To overcome these challenges, we developed ImputeGAP, a versatile and comprehensive library for time series imputation. ImputeGAP supports a wide range of imputation algorithms and modular missing data simulation, catering to datasets with varying characteristics. It also streamlines imputation analysis with features such as automated hyperparameter tuning, benchmarking, explainability, and downstream evaluation.

With the emergence of The Internet of Things (IoT), smart sensors have become abundant in our daily lives. Failures are very common in those devices, leaving the recorded time series with missing blocks of consecutive values. A cottage industry of imputation algorithms exists, each with different performance tradeoffs. The diversity in time series features, missingness patterns, and algorithms' categories makes it challenging to select the best algorithm. In this demonstration, we showcase ImputeVIS, an analytical tool for benchmarking imputation algorithms. ImputeVIS provides an optimal configuration of those algorithms by implementing various AutoML parameterization strategies. Moreover, it uncovers the behavior of imputation algorithms by explaining the interplay between time series features and the imputation results. Its interactive web browser interface allows users to simulate real-world sensor malfunctions by contaminating time series with different missing block scenarios, deploy their imputation algorithms, and compare them against various popular imputation families.