  Time series generation is a crucial research topic in the area of deep
learning, which can be used for data augmentation, imputing missing values, and
forecasting. Currently, latent diffusion models are ascending to the forefront
of generative modeling for many important data representations. Being the most
pivotal in the computer vision domain, latent diffusion models have also
recently attracted interest in other communities, including NLP, Speech, and
Geometric Space. In this work, we propose TimeLDM, a novel latent diffusion
model for high-quality time series generation. TimeLDM is composed of a
variational autoencoder that encodes time series into an informative and
smoothed latent content and a latent diffusion model operating in the latent
space to generate latent information. We evaluate the ability of our method to
generate synthetic time series with simulated and realistic datasets, benchmark
the performance against existing state-of-the-art methods. Qualitatively and
quantitatively, we find that the proposed TimeLDM persistently delivers
high-quality generated time series. Sores from Context-FID and Discriminative
indicate that TimeLDM consistently and significantly outperforms current
state-of-the-art benchmarks with an average improvement of 3.4$\times$ and
3.8$\times$, respectively. Further studies demonstrate that our method presents
better performance on different lengths of time series data generation. To the
best of our knowledge, this is the first study to explore the potential of the
latent diffusion model for unconditional time series generation and establish a
new baseline for synthetic time series.
