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:
https://github.com/ML4ITS/TimeVQVAE.

提出了一种名为 TimeVQVAE 的新模型，它使用矢量量化技术解决时间序列生成（TSG）问题，通过在时频域中分离出低频和高频来保留时间序列的重要特征，并使用双向转换模型学习离散潜在空间的先验知识，使其具有更好的全局时间一致性和更好的质量。