DAGAN:Generative Adversarial Network with Dual Attention-Enhanced GRU for Multivariate Time Series Imputation

Abstract

Missing values are common in multivariate time series data, which limits the usability of the data and impedes further analysis. Thus, it is imperative to impute missing values in time series data. However, in handling missing values, existing imputation techniques fail to take full advantage of the time-related data and have limitations in capturing potential correlations between variables. This paper presents a new model for imputing multivariate time series data called DAGAN, which comprises a generator and a discriminator. Specifically, the generator incorporates a Temporal Attention layer, a Relevance Attention layer, and a Feature Aggregation layer. The Temporal Attention layer utilizes an attention mechanism and recurrent neural network to address the RNN’s inability to model long-term dependencies in the time series. The Relevance Attention layer employs a self-attention-based network architecture to capture correlations among multiple variables in the time series. The Feature Aggregation layer integrates time information and correlation information using a residual network and a Linear layer for effective imputation of missing data. In the discriminator, we also introduce a temporal cueing matrix to aid in distinguishing between generated and real values. To evaluate the proposed model, we conduct experiments on two real-time series datasets, and the findings indicate that DAGAN outperforms state-of-the-art methods by more than 13$\%$.

This work was supported by the National Key R &D Program of China under Grant No. 2020YFB1710200 and Heilongjiang Key R &D Program of China under Grant No. GA23A915.