用大模型分层提取时间序列的局部与全局特征，提升预测精度
Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting
- 分层利用大模型：浅层捕获短期变化，深层捕捉长期趋势
- 引入轻量混合模块，融合多尺度特征，性能超越主流方法
- 适合少样本和零样本场景，计算开销小，实用性强
时间序列预测在多个领域至关重要，其数据同时包含局部模式与全局依赖。尽管基于Transformer的方法能有效捕捉全局依赖，却常忽略短期局部变化。近期将大语言模型（LLM）应用于时间序列预测的方法，仍将其视为黑箱编码器，仅依赖最终层输出，未能充分利用层次化表示。为此，我们提出Logo-LLM，一种新型基于大模型的框架，可显式从预训练大模型的不同层级中提取并建模多尺度时间特征。实证分析表明，大模型浅层捕捉时间序列的局部动态，深层则编码全局趋势。此外，Logo-LLM引入轻量级局部混合器（Local-Mixer）与全局混合器（Global-Mixer）模块，实现跨层级特征与时间输入的对齐与融合。大量实验显示，Logo-LLM在多种基准上表现优异，在少样本与零样本设置下均具备强泛化能力，且计算开销低。
原文摘要 · Abstract (English)
Time series forecasting is critical across multiple domains, where time series data exhibit both local patterns and global dependencies. While Transformer-based methods effectively capture global dependencies, they often overlook short-term local variations in time series. Recent methods that adapt large language models (LLMs) into time series forecasting inherit this limitation by treating LLMs as black-box encoders, relying solely on the final-layer output and underutilizing hierarchical representations. To address this limitation, we propose Logo-LLM, a novel LLM-based framework that explicitly extracts and models multi-scale temporal features from different layers of a pre-trained LLM. Through empirical analysis, we show that shallow layers of LLMs capture local dynamics in time series, while deeper layers encode global trends. Moreover, Logo-LLM introduces lightweight Local-Mixer and Global-Mixer modules to align and integrate features with the temporal input across layers. Extensive experiments demonstrate that Logo-LLM achieves superior performance across diverse benchmarks, with strong generalization in few-shot and zero-shot settings while maintaining low computational overhead.
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