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）应生成符合被建模数据潜在分布的合成数据。对于实值时序数据，这需要同时捕获数据的静态分布，以及任何潜在时间范围内的完整时序分布。这一时序要素导致问题更为复杂，可能使现有解决方案约束不足、训练不稳定，或易出现不同程度的模式坍塌。在FETSGAN中，整个序列通过序列到序列风格的对抗自编码器（AAE）直接映射到生成器的采样空间，其中对抗训练用于在特征空间和低维采样空间中匹配训练分布。这一额外约束提供了松散性保证，确保合成样本的时序分布不会坍塌。此外，引入首超阈值（FAT）算子以增强编码序列的重建，从而提升训练稳定性与生成合成数据的整体质量。这些创新贡献表明，在时序相似性的定性指标和通过FETSGAN生成数据的定量预测能力方面，相比当前最先进的对抗学习方法具有显著改进。