Fast Multivariate Time Series Generation via Sampling from a Spherical Manifold

We propose a multivariate time series generator that embeds the training data onto a uniformly populated spherical manifold, from which new points are sampled and decoded into high-fidelity, diverse samples. The approach uses no attention mechanisms, adversarial training, diffusion, flow matching, or vector quantization, yet achieves generation quality competitive with the state of the art while sampling one to four orders of magnitude faster. We identify which ingredients are essential to the success of the approach in time series generation, and which are not. A controlled ablation shows that replacing the Gaussian prior of a variational autoencoder with the bounded sphere is what makes the approach competitive, and we find that uniform coverage of the sphere is critical to generation quality, which we enforce with a KoLeo regularizer. Conversely, iterative refinement and consistency regularization prove unnecessary. The model is also readily made class-conditional, and its samples are effective for data augmentation in long-term (128-step) human activity recognition. Our results suggest that a bounded spherical manifold is a natural fit for time series generation, and they motivate further research in this direction.