测试大模型在时间序列分析中的表现，发现其在异常检测上有效，但预测任务中不如简单模型。
Are Large Language Models Useful for Time Series Data Analysis?
- 用GPT4TS和自回归模型对比分析时间序列数据
- 异常检测效果好，但预测任务性能与简单模型相当
- 适合关注时序异常检测的研究者参考
时间序列数据在医疗、能源、金融等领域至关重要，常用于分类、异常检测和预测等任务。近年来，大语言模型（LLMs）因其处理复杂数据的能力受到关注。本研究通过GPT4TS和自回归模型，在多个基准数据集上对比了LLM与非LLM方法在分类、异常检测和预测三类任务上的表现，评估其准确率、精确率和泛化能力。结果表明，虽LLM在异常检测任务中表现突出，但在预测任务中，其优势不明显，部分情况下简单模型表现更佳或相当。研究揭示了LLM在时间序列分析中的潜力与局限，为后续系统性探索提供了基础。
原文摘要 · Abstract (English)
Time series data plays a critical role across diverse domains such as healthcare, energy, and finance, where tasks like classification, anomaly detection, and forecasting are essential for informed decision-making. Recently, large language models (LLMs) have gained prominence for their ability to handle complex data and extract meaningful insights. This study investigates whether LLMs are effective for time series data analysis by comparing their performance with non-LLM-based approaches across three tasks: classification, anomaly detection, and forecasting. Through a series of experiments using GPT4TS and autoregressive models, we evaluate their performance on benchmark datasets and assess their accuracy, precision, and ability to generalize. Our findings indicate that while LLM-based methods excel in specific tasks like anomaly detection, their benefits are less pronounced in others, such as forecasting, where simpler models sometimes perform comparably or better. This research highlights the role of LLMs in time series analysis and lays the groundwork for future studies to systematically explore their applications and limitations in handling temporal data.
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