Multivariate time series are widely used in industrial equipment monitoring and maintenance, health monitoring, weather forecasting and other fields. Due to abnormal sensors, equipment failures, environmental interference and human errors, the collected multivariate time series usually have certain missing values. Missing values imply the regularity of data, and seriously affect the further analysis and application of multivariate time series. Conventional imputation methods such as statistical imputation and machine learning-based imputation cannot learn the latent relationships of data and are difficult to use for missing values imputation in multivariate time series. This paper proposes a novel Time and Location Gated Recurrent Unit (TLGRU), which takes into account the non-fixed time intervals and location intervals in multivariate time series and effectively deals with missing values. We made necessary modifications to the architecture of the end-to-end imputation model \({E}^{2}\) GAN and replaced Gated Recurrent Unit for Imputation (GRUI) with TLGRU to make the generated fake sample closer to the original sample. Experiments on a public meteorologic dataset show that our method outperforms the baselines on the imputation accuracy and achieves a new state-of-the-art result.

多元时间序列广泛应用于工业设备监测与维护、健康监测、天气预报等领域。由于传感器异常、设备故障、环境干扰和人为错误等原因，采集到的多元时间序列通常存在一定的缺失值。缺失值意味着数据的规律性，严重影响多元时间序列的进一步分析和应用。传统的插补方法，如统计插补和基于机器学习的插补，无法学习数据的潜在关系，难以用于多元时间序列中的缺失值插补。本文提出了一种新颖的时间和位置门控循环单元（TLGRU），它考虑了多元时间序列中的非固定时间间隔和位置间隔，有效地处理了缺失值。我们对端到端插补模型的架构进行了必要的修改\({E}^{2}\) GAN 并将 Gated Recurrent Unit for Imputation (GRUI) 替换为 TLGRU，以使生成的假样本更接近原始样本。在公共气象数据集上的实验表明，我们的方法在插补精度方面优于基线，并取得了新的最先进的结果。