Time series generation (TSG) studies have mainly focused on the use of Generative Adversarial Networks (GANs) combined with recurrent neural network (RNN) variants. However, the fundamental limitations and challenges of training GANs still remain. In addition, the RNN-family typically has difficulties with temporal consistency between distant timesteps. Motivated by the successes in the image generation (IMG) domain, we propose TimeVQVAE, the first work, to our knowledge, that uses vector quantization (VQ) techniques to address the TSG problem. Moreover, the priors of the discrete latent spaces are learned with bidirectional transformer models that can better capture global temporal consistency. We also propose VQ modeling in a time-frequency domain, separated into low-frequency (LF) and high-frequency (HF). This allows us to retain important characteristics of the time series and, in turn, generate new synthetic signals that are of better quality, with sharper changes in modularity, than its competing TSG methods. Our experimental evaluation is conducted on all datasets from the UCR archive, using well-established metrics in the IMG literature, such as Fr\'echet inception distance and inception scores. Our implementation on GitHub: \url{https://github.com/ML4ITS/TimeVQVAE}.

翻译：时间序列生成（TSG）研究主要集中于将生成对抗网络（GAN）与循环神经网络（RNN）变体相结合。然而，GAN训练的根本性局限与挑战依然存在。此外，RNN系列模型通常难以处理远时间步长之间的时间一致性。受图像生成（IMG）领域成功经验的启发，我们首次提出TimeVQVAE方法，该工作据我们所知首次将向量量化（VQ）技术应用于TSG问题。同时，通过双向Transformer模型学习离散潜在空间的先验分布，从而更有效地捕获全局时间一致性。我们还提出在时频域中进行VQ建模，将信号分解为低频（LF）与高频（HF）分量。这使得我们能够保留时间序列的重要特征，进而生成比现有TSG方法质量更高、模块性变化更剧烈的新型合成信号。我们在UCR数据仓库的所有数据集上开展实验评估，采用图像生成文献中广泛认可的指标，如Fr\'echet初始距离和初始分数。我们的代码实现发布在GitHub：\url{https://github.com/ML4ITS/TimeVQVAE}。