Can LLMs improve text-controlled time-series generation?
BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modelling
This paper introduces BRIDGE, a framework for generating realistic and controllable time series data guided by text descriptions. It addresses the scarcity of paired text-time series datasets by using a novel multi-agent system to generate and refine text descriptions. This system utilizes LLMs to collect templates, evaluate their effectiveness in downstream time series forecasting tasks, and refine the templates through iterative feedback and collaboration among multiple agent teams with distinct roles (manager, planner, scientist, engineer, observer). BRIDGE then uses these refined text descriptions along with learned semantic prototypes as input to a diffusion model to generate the time series. Key to its effectiveness is the hybrid approach combining explicit information from text with implicit domain knowledge captured by the prototypes. The multi-agent system improves the quality of generated text for conditioning the diffusion model, and the combined approach achieves state-of-the-art performance in controlled time series generation across various datasets and domains.