Red tide data are typical multivariate time series (MTS) and complete data help analyze red tide more conveniently. However, missing values due to artificial or accidental events hinder further analysis of red tide phenomenon. Generative adversarial network (GAN) is effective in capturing distribution of MTS while the imputation performance is far from satisfactory, especially in conditions of high missing rate. One of the remaining open challenges is that common GAN-based imputation methods usually lack the ability to excavate implicit correlations between different attributions and downstream tasks, from which advanced latent information about missing values can be mined to improve imputation performance. To deal with the problem, a novel multi-task learning-based generative adversarial imputation network (MTGAIN) is proposed by introducing the prediction task into GAN to unearth more detailed information about missing values to better model distribution of red tide MTS. Furthermore, the homoscedastic uncertainty of multiple tasks is exploited to balance the weights of losses between generation and prediction tasks. The experiments conducted on a real-world dataset demonstrate that MTGAIN outperforms existing methods in terms of imputation and post-imputation performances, especially in conditions of high missing rate.

赤潮数据是典型的多变量时间序列（MTS），完整的数据有助于更方便地分析赤潮。然而，由于人为或意外事件导致的缺失值阻碍了对赤潮现象的进一步分析。生成对抗网络（GAN）在捕捉 MTS 分布方面是有效的，但插补性能远不能令人满意，尤其是在高缺失率的情况下。剩下的开放挑战之一是基于 GAN 的常见插补方法通常缺乏挖掘不同属性和下游任务之间隐含相关性的能力，从中可以挖掘有关缺失值的高级潜在信息以提高插补性能。为了解决问题，提出了一种新颖的基于多任务学习的生成对抗插补网络（MTGAIN），将预测任务引入 GAN，以挖掘更多关于缺失值的详细信息，从而更好地模拟赤潮 MTS 的分布。此外，利用多个任务的同方差不确定性来平衡生成任务和预测任务之间的损失权重。在真实数据集上进行的实验表明，MTGAIN 在插补和插补后性能方面优于现有方法，尤其是在高缺失率的情况下。利用多个任务的同方差不确定性来平衡生成任务和预测任务之间的损失权重。在真实数据集上进行的实验表明，MTGAIN 在插补和插补后性能方面优于现有方法，尤其是在高缺失率的情况下。利用多个任务的同方差不确定性来平衡生成任务和预测任务之间的损失权重。在真实数据集上进行的实验表明，MTGAIN 在插补和插补后性能方面优于现有方法，尤其是在高缺失率的情况下。