Sensor data streams occur widely in various real-time applications in the context of the Internet of Things (IoT). However, sensor data streams feature missing values due to factors such as sensor failures, communication errors, or depleted batteries. Missing values can compromise the quality of real-time analytics tasks and downstream applications. Existing imputation methods either make strong assumptions about streams or have low efficiency. In this study, we aim to accurately and efficiently impute missing values in data streams that satisfy only general characteristics in order to benefit real-time applications more widely. First, we propose a message propagation imputation network (MPIN) that is able to recover the missing values of data instances in a time window. We give a theoretical analysis of why MPIN is effective. Second, we present a continuous imputation framework that consists of data update and model update mechanisms to enable MPIN to perform continuous imputation both effectively and efficiently. Extensive experiments on multiple real datasets show that MPIN can outperform the existing data imputers by wide margins and that the continuous imputation framework is efficient and accurate.

本研究提出了一种基于消息传播的缺失值填补网络 (MPIN)，能够有效地恢复数据流中数据实例的缺失值，并通过连续填补框架实现持续的高效填补，实验证明 MPIN 在多个真实数据集上能够显著优于现有的数据填补方法，并且连续填补框架具有高效准确的特点。

多属性传感器数据流的缺失值填充通过信息传播（扩展版）