Temporal Harmony: Bridging Gaps in Multivariate Time Series Data with GAN-Transformer Integration

Multivariate Time Series (MTS) data imputation plays a pivotal role in enhancing the robustness of temporal data analyses across diverse domains. In this paper, we propose a novel hybrid model that combines the generative ability of Generative Adversarial Networks (GANs) with the sequence modeling capabilities of Transformers for MTS data imputation. The generator of the GAN synthesizes realistic MTS data, while the discriminator ensures authenticity by creating an adversarial training dynamic. Simultaneously, a Transformer processes both observed and synthetic MTS data, leveraging its attention mechanisms to capture intricate temporal dependencies and generate imputations for missing values. The joint optimization of the GAN and Transformer components fosters a synergistic model, proficient in generating contextually accurate missing values. We detail the architecture, training process, and hyperparameters of the proposed model, evaluating its performance on diverse MTS datasets. Our model aims to advance the state-of-the-art in MTS data imputation, addressing challenges practically through the fusion of GANs and Transformers. Experimental results showcase promising imputation accuracy and underscore the potential of the hybrid approach in temporal data analytics.