Real-Time Imputation Model for Missing Sensor Data Based on Alternating Attention Mechanism

In fields like healthcare and finance, multivariate time-series (MTS) data frequently encounter a growing number and complexity of missing values, hindering the application of advanced analytical methods. Effective imputation of missing sensor data is essential for accurate data analysis. In the MTS, the variable changing with time creates the time dependence, and the variables at each moment have a coupling relationship and influence each other. At the same time, the missing series has a similar representation ability to the complete series. Based on these time-series characteristics, this article establishes an MTS imputation model based on the attention mechanism and contrastive learning. The attention model is used to deeply extract the temporal dependencies as well as the dependencies among the variables, while contrastive learning is employed to ensure that missing series share similar feature representations with the original series, thereby enhancing the model's imputation performance for incomplete series. Finally, after conducting extensive experiments on two real-world MTS datasets that contain five missing patterns, and when compared to state-of-the-art (SOTA) methods, the model performed well, with 13 metrics achieving first place. Our method aims to provide a better database for tasks such as anomaly detection, classification, and prediction of MTS.