ImputeGAP整合多种插补方法，支持真实缺失模式模拟与下游评估。
ImputeGAP: A Comprehensive Library for Time Series Imputation
- 提供多种插补算法与可模块化缺失模拟
- 支持自动化调参、基准测试与下游任务评估
- 适合需要可靠插补流程的研究者和工程师
随着传感器故障频发，时间序列数据填补（imputation）已成为预处理的核心环节。尽管已有众多填补算法被开发，现有时间序列库对填补的支持仍有限，且常无法模拟真实的缺失模式，也未考虑填补结果对下游分析的影响。本文提出ImputeGAP，一个全面的时间序列填补库，支持多样化的填补方法与模块化缺失数据模拟，适用于不同特性的数据集。该库包含自动化超参数调优、基准测试、可解释性分析、下游评估功能，并兼容主流时间序列框架。
原文摘要 · Abstract (English)
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. The library includes extensive customization options, such as automated hyperparameter tuning, benchmarking, explainability, downstream evaluation, and compatibility with popular time series frameworks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。