Missing Data Recovery Methods on Multivariate Time Series in IoT: A Comprehensive Survey

This survey comprehensively examines the challenges and methodologies for missing data recovery in Multivariate Time Series (MTS) within the context of Internet of Things (IoT) environments. It delves into the distinct mechanisms and patterns of missing data that are prevalent in IoT MTS, providing a detailed categorization of existing methods into three primary frameworks: non-data mining, traditional data mining, and deep learning approaches. Each category is critically analyzed to highlight strategic improvements tailored to MTS data and their applicability to various missing patterns of IoT. The paper also discusses the computational demand, convergence speed, real-time capability, and adaptability to missing patterns of these methods, offering a thorough evaluation of their strengths and weaknesses. By synthesizing current methodologies and identifying gaps in the literature from 2013 to 2024, this work identifies future research directions aimed at enhancing the robustness and efficiency of missing data recovery in complex IoT systems.