  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}.
