A Hands-on Tutorial on Time Series Imputation with ImputeGAP

摘要

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. In this tutorial, we will provide an engaging hands-on tutorial where you will learn time series imputation using the powerful Python library, ImputeGAP. The session is divided into two parts. In the first part, we will dive into building an end-to-end imputation workflow with the library, where you will explore real-world missingness patterns simulation, leverage automated tuning for optimal imputation, and benchmark imputation techniques-all with extensive customization options. In the second part, we will unlock advanced functionalities, including assessing the impact of imputation on downstream analytics and understanding how time series features influence imputation outcomes. Whether you are a researcher or practitioner, this tutorial will provide you with the expertise to handle missing data in time series. The ImputeGAP library is accessible at: https://imputegap.readthedocs.io.