Generative Adversarial Networks (GANs) should produce synthetic data that fits the underlying distribution of the data being modeled. For real valued time-series data, this implies the need to simultaneously capture the static distribution of the data, but also the full temporal distribution of the data for any potential time horizon. This temporal element produces a more complex problem that can potentially leave current solutions under-constrained, unstable during training, or prone to varying degrees of mode collapse. In FETSGAN, entire sequences are translated directly to the generator's sampling space using a seq2seq style adversarial auto encoder (AAE), where adversarial training is used to match the training distribution in both the feature space and the lower dimensional sampling space. This additional constraint provides a loose assurance that the temporal distribution of the synthetic samples will not collapse. In addition, the First Above Threshold (FAT) operator is introduced to supplement the reconstruction of encoded sequences, which improves training stability and the overall quality of the synthetic data being generated. These novel contributions demonstrate a significant improvement to the current state of the art for adversarial learners in qualitative measures of temporal similarity and quantitative predictive ability of data generated through FETSGAN.

翻译：生成对抗网络（GANs）应生成符合所建模数据潜在分布的合成数据。对于实值时间序列数据，这意味着需要同时捕捉数据的静态分布，以及任意时间跨度上的完整时间分布。这一时间要素产生了更为复杂的问题，可能导致现有方案约束不足、训练不稳定，或易出现不同程度的模式坍塌。在FETSGAN中，完整序列通过seq2seq风格的对抗自编码器（AAE）直接映射到生成器的采样空间，利用对抗训练在特征空间和低维采样空间中对齐训练分布。这一额外约束为合成样本的时间分布不会坍塌提供了松散保障。此外，引入首次超阈值（FAT）算子以增强编码序列的重建，从而提升训练稳定性及生成合成数据的整体质量。这些创新贡献在时间相似性的定性度量以及FETSGAN生成数据的定量预测能力上，显著超越了当前对抗学习领域的最先进水平。