用多尺度分解+大模型提升时间序列预测精度
LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
- 将时间序列按不同周期分解，再用预训练大模型处理
- 在多个数据集上优于当前最佳模型，跨预测周期表现稳定
- 适合需要高精度、可扩展预测的工业场景
时间序列预测仍是挑战性任务，尤其在复杂多尺度时序模式下。本文提出LLM-Mixer框架，通过结合多尺度时间序列分解与预训练大语言模型（LLMs），提升预测准确性。该方法将数据分解为多个时间分辨率，利用冻结的LLM在文本提示引导下处理各尺度特征，有效捕捉短期波动与长期趋势。在多变量和单变量数据集上的大量实验表明，LLM-Mixer在多种预测时长远超近期先进模型。本工作展示了多尺度分析与大模型结合在高效、可扩展时间序列预测中的潜力。
原文摘要 · Abstract (English)
Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series decomposition with pre-trained LLMs (Large Language Models). LLM-Mixer captures both short-term fluctuations and long-term trends by decomposing the data into multiple temporal resolutions and processing them with a frozen LLM, guided by a textual prompt specifically designed for time-series data. Extensive experiments conducted on multivariate and univariate datasets demonstrate that LLM-Mixer achieves competitive performance, outperforming recent state-of-the-art models across various forecasting horizons. This work highlights the potential of combining multiscale analysis and LLMs for effective and scalable time-series forecasting.
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