PrismFlow augments flow matching with residual dynamical experts and a winner-take-all objective to reduce spectral distortion and improve mode coverage in time-series generation.
T2s: High-resolution time series generation with text-to-series diffusion models.arXiv preprint arXiv:2505.02417
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ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
A semi-synthetic dataset of 7 million context-augmented time series windows with verifier-filtered contexts enables transfer to real-world context-aided forecasting and suggests data quality—not architecture—was the bottleneck.
UPLOTS proposes a unified prompt-guided pretrained transformer for generating constrained time-series data across diverse domains using dynamic multi-dataset loss re-weighting.
citing papers explorer
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PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation
PrismFlow augments flow matching with residual dynamical experts and a winner-take-all objective to reduce spectral distortion and improve mode coverage in time-series generation.
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Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
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Overcoming the Modality Gap in Context-Aided Forecasting
A semi-synthetic dataset of 7 million context-augmented time series windows with verifier-filtered contexts enables transfer to real-world context-aided forecasting and suggests data quality—not architecture—was the bottleneck.
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UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation
UPLOTS proposes a unified prompt-guided pretrained transformer for generating constrained time-series data across diverse domains using dynamic multi-dataset loss re-weighting.