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

Abstract

Time series forecasting models often face challenges in cross-domain fine-tuning, such as high training costs and limited adaptability. To address these issues, we propose a Cue-driven Feature Fusion Network (CFF-Net), which combines semantic cues from textual prompts with numerical time series features for parameter-efficient adaptation. 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. Specifically, CFF-Net includes three main components. First, the Semantic Prompt Encoding Module (SPM) transforms numerical sequences into temporally relevant natural language descriptions, which are processed by GPT-2 to extract semantic representations. Second, the Dynamic Semantic Modulation Module (DSM) maps these semantic representations into learnable scaling (γ) and shifting (β) factors through a multi-layer perceptron, enabling modulation of PatchTST predictions within the Scale-and-Shift Feature (SSF) mechanism. Finally, a warm-start strategy is used to stabilize semantic integration during training. Experimental results on three public datasets and the TCTS dataset show that CFF-Net achieves lower errors than PatchTST in many settings, although the improvements are not uniform across all datasets and metrics. For example, on the Weather dataset, CFF-Net reduces MSE by 12.50% and 11.88% under the 30% and 20% training-sample settings, respectively. On the TCTS dataset, the corresponding MSE reductions are 5.83% and 6.60%. These results suggest that semantic prompt guidance can improve forecasting performance in several limited-data scenarios while keeping most backbone parameters fixed.