Sammendrag
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 Frechet inception ´
distance and inception scores.
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