Abstract: Gas chromatography data of oil-immersed transformers is a kind of multivariate time series, but such data is often missing due to equipment or network failures. Imputation is usually required to form a complete dataset for further business analysis and research. However, the existing imputation models cannot deal with multivariate time series data conveniently to guarantee the efficiency and effect from the inherent characteristics of temporal irregularity and temporal bidirectionality. In this paper, a model Conv-WGAIN is proposed based on the Generative Adversarial Imputation Nets (GAIN). Through the constructed imputation feature map, 2D convolution can be used to learn temporal bidirectional features and simultaneously deal with irregular time intervals. The Wasserstein distance is introduced in discriminator for judgement to improve the stability of the model. Experiments on gas chromatography datasets from a real project and 3 public datasets show that our work is universal for data imputation on multivariate time series missing, and Conv-WGAIN outperforms other baselines with 20.75% to 73.37% in metric RMSE. 

Key words: generative adversarial imputation nets, multivariate time series data, convolutional neural network, Wasserstein distance, missing value imputation