UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation

• URL: http://arxiv.org/abs/2606.10466v2
• Date: Wed, 17 Jun 2026 09:27:02 GMT
• Title: UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation
• Abstract summary: We propose UPLOTS, a Unified, Prompt-guided Language framework fOr constrained Time-Series Generation.<n>Instead of building task-specific models, UPLOTS leverages a single pre-trained transformer backbone guided by learned constraint prompts.<n>We evaluate UPLOTS on four real-world benchmarks and multiple constraint settings, including peak-period, calendar, load-level, and volatility patterns.
• Score: 9.651308133504818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In time-series generation, existing approaches typically handcraft ortrain a separate model for each dataset, which hinders their scalability and fails to leverage shared temporal structures across domains. To address this fragmentation, we propose UPLOTS, a Unified, Prompt-guided Language model framework fOr constrained Time-Series Generation across diverse domains. Instead of building task-specific models, UPLOTS leverages a single pre-trained transformer backbone guided by learned constraint prompts, enabling on-demand generation with precise pattern control. One key innovation is our dynamic multi-dataset loss re-weighting and prompt-to-pattern mapping, which allows UPLOTS to internalize diverse temporal structures during training and conditionally generate them at inference. We evaluate UPLOTS on four real-world benchmarks and multiple constraint settings, including peak-period, calendar, load-level, and volatility patterns. Additional held-out constraint-combination and downstream forecasting experiments further demonstrate that UPLOTS generalizes beyond the original peak-pattern setting and improves data augmentation under scarce real-data regimes. Our code and baselines are available at anonymous github repo: https://anonymous.4open.science/r/UPLOTS-6C36.

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