Missing sequence data prevent local data from reflecting the overall distribution of a sample, hindering data analysis. The problem of missing data during actual production is a serious issue and results in a high defect rate, low dimensionality, and high noise level. In this study, a Masked Generative Adversarial Network (MAGAN) model is proposed that is less affected by the data loss rate than a baseline comparison model, and at an 80% missing data rate, the model can still better reflect the distribution of real data. MAGAN shows better results than a traditional processing method for dealing with missing data.

中文翻译：

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MAGAN：用于处理丢失的IoT序列数据的蒙版自动编码器生成对抗网络

缺少序列数据会阻止本地数据反映样品的总体分布，从而妨碍数据分析。在实际生产期间丢失数据的问题是一个严重的问题，并导致高缺陷率，低尺寸和高噪声水平。在这项研究中，提出了一种蒙代式生成对抗网络（MAGAN）模型，该模型比基线比较模型受数据丢失率的影响较小，并且在丢失率达到80％的情况下，该模型仍可以更好地反映真实数据的分布。与处理丢失数据的传统处理方法相比，MAGAN显示出更好的结果。