用粗略形状引导精炼，让大模型预测时间序列更准更快
Efficient Test-Time Scaling for LLM-based Time Series Forecasting
- 先用轻量Transformer预测未来趋势轮廓，再用大模型逐级修正细节
- 在长时序预测上超越基线，推理耗时降低显著
- 适合追求高精度与低计算成本的时序预测场景
长期时间序列预测需保留全局结构如趋势与周期性。现有基于大模型的预报方法常通过测试时缩放（如迭代精炼）提升精度，但计算开销大，且随预测范围扩大，全局形状偏差加剧。本文提出SCALER，一种从粗到细的预测框架：首先用轻量Transformer生成未来动态的粗略表示；该形状作为紧凑引导，驱动大模型通过迭代残差令牌精炼进行测试时缩放，每步处理的标记数大幅减少。通过显式未来形状预测引导精炼过程，降低对长提示词的依赖，固定步数精炼避免了昂贵的奖励模型选择，进一步降低计算开销。实验表明，SCALER在长期、短期及零样本预测任务中均优于强基线，同时显著降低大模型缩放带来的推理成本。
原文摘要 · Abstract (English)
Long-term time series forecasting benefits from preserving global structure such as trends and seasonality. Recent LLM-based forecasters often improve accuracy through test-time scaling (e.g., iterative refinement), but these methods are computationally expensive and increasingly prone to global-shape mismatch as the prediction horizon extends. We propose SCALER, a coarse-to-fine forecasting framework that first employs a lightweight Transformer tailored to long-term shape modeling to predict a coarse representation of future dynamics. This predicted shape then serves as a compact guide for an LLM to perform test-time scaling via iterative coarse-to-fine residual token refinement, while processing substantially fewer tokens at each step. By guiding refinement with an explicit future-shape prediction, SCALER reduces reliance on long description prompts, and its fixed-step refinement avoids costly reward-model-based selection, further lowering computational overhead. Experimental results demonstrate that SCALER outperforms strong forecasting baselines in long-term, short-term and zero-shot forecasting while significantly reducing the inference cost associated with scaled LLM for time series forecasting. Code: https://github.com/xuanmay2701/SCALER.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。