Time series generation with spectrally aligned latent flow matching
Latent flow models have proven to be a reliable and cost-effective method for time series generation. However, the latent compression induces unwanted artefacts, such as a spectral mismatch with respect to the underlying dataset, thus hindering their use as training surrogates. In this article, we propose a spectrally-aligned latent-flow time series generator, where the latent space for flow matching is trained to preserve dynamical properties that are relevant for the suitability of synthetic samples. We find that incorporating fine-tuning losses based on canonical signal representations such as the Fourier, wavelet and signature transforms helps overcome these issues. The interpretabili...
| Authors | Camilo Carvajal Reyes, Felipe Tobar |
|---|---|
| Primary category | cs.LG |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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