AttnWGAIN: Attention-Based Wasserstein Generative Adversarial Imputation Network for IoT Multivariate Time Series

The missing values pervasively exist in IoT multivariate time series, which severely impairs the interpretability of the data and poses a major problem for advanced analysis and pattern recognition tasks. Existing IoT time series imputation methods typically address missing values through deletion, statistical estimation, or deep-learning-based imputation However, these methods often fail to sufficiently explore temporal information or are multi-stage. In this paper, we introduce a novel model for IoT time series imputation, called AttnWGAIN, which utilizes an attention mechanism to capture both temporal and feature correlations in the data and imputes missing values (MVs) in a generative manner. Specifically, AttnWGAIN is a GAN-based model where the generator leverages a number of diagonal masked attention blocks to reconstruct an IoT complete time series, and the discriminator attempts to distinguish between real (observed) and fake (imputed) elements to enhance imputation accuracy. Furthermore, by training the model with a joint optimization strategy, AttnWGAIN demonstrates superior performance in IoT time series imputation. We conduct extensive experiments on four real-world IoT datasets, and the results quantitatively and qualitatively show that AttnWGAIN outperforms state-of-the-art methods in IoT time series imputation tasks. The source code is available at https://github.com/wzq-come-on/AttnWGAIN.git.