GenG: An LLM-Based Generic Time Series Data Generation Approach for Edge Intelligence via Cross-Domain Collaboration

In this paper, we propose GenG, a generic time series data generation approach for edge intelligence that incor-porates knowledge from different domains to synthesize high-fidelity and controllable time series data resembling to different IoT devices. Specifically, GenG decomposes the time series data generation task into two subtasks, the first sub task is to finetune a Large Language Model (LLM) in a self-training method to harness its outstanding knowledge and reasoning capacities for explainable data generation, solving the problem of what to generate. The second one focuses on generating high-quality and controllable time series data conditioning on the output of the finetuned LLM, solving the problem of how to generate. Furthermore, a two-stage generation process is proposed to increase the quality of the generation results by introducing both the abstract and detailed guidance signals, which also enables flexible control over the generation results and ensures synthesized data with consistent features. During deployment, GenG can be arranged in a cloud-edge collaboration way, where the cumbersome LLM and light-weight generation model are placed on the cloud and edge, respectively, fitting well with the resource-constrained edge intelligence. Experimental results in different generation tasks demonstrate GenG's efficiency in reasoning about the generation task and synthesizing high-fidelity time series data with controllable features.