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

UPLOTS, a Unified Pretrained Language model for constrained Time-series Generation, has been introduced to streamline the generation of time-series data across various domains using a single pre-trained transformer backbone. Key innovations include dynamic multi-dataset loss re-weighting and prompt-to-pattern mapping, which enhance the model's ability to internalize and generate diverse temporal structures. Evaluated on four real-world benchmarks, UPLOTS demonstrates improved generalization and data augmentation capabilities, making it a valuable tool for practitioners facing challenges with task-specific models in time-series generation.