Jianqiang Jing

Sample-efficient fine-tuning with textual prompts for time series forecasting

The main idea is to use the semantic representation ability of large language models to provide auxiliary guidance, while dynamically modulating numerical predictions through scale-and-shift operations, to improve forecasting performance in several limited-data scenarios while keeping most backbone parameters fixed.