arXiv (All)
open access
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 p
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Time series generation with spectrally aligned latent flow matching#1006840
- HALO: Enhancing Time Series Generation via Hyperspherical Latents and Masked AutoregRessive Modeling#1131662
- ChronoFlow: Hierarchical Flow Matching for Irregular Time Series Generation#1117988
- DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning#999979
- Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching#1116763
Record · ID 1036377
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive