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Abstract 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 $${E}^{2}$$ 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.

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. [ABSTRACT FROM AUTHOR]