A Hands-on Tutorial on Time Series Imputation with ImputeGAP

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.